From e51a3ae65e3401b21de860c90a64102133d2d6a6 Mon Sep 17 00:00:00 2001
From: Cheng Wan <54331508+ch-wan@users.noreply.github.com>
Date: Sun, 30 Aug 2026 02:24:35 -0700
Subject: [PATCH] [Config] Round 5.2: the per-model declarations get their own
 modules (#37087)

Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
---
 .../srt/arg_groups/model_override_base.py     |  376 ++++
 .../arg_groups/model_overrides/__init__.py    |   37 +
 .../arg_groups/model_overrides/deepseek_v2.py |  145 ++
 .../arg_groups/model_overrides/deepseek_v4.py |   76 +
 .../srt/arg_groups/model_overrides/exaone.py  |   23 +
 .../arg_groups/model_overrides/falcon_h1.py   |   22 +
 .../model_overrides/gemma2_gemma3.py          |   29 +
 .../srt/arg_groups/model_overrides/gemma4.py  |   47 +
 .../arg_groups/model_overrides/glm4_moe.py    |   50 +
 .../srt/arg_groups/model_overrides/gpt_oss.py |  127 ++
 .../model_overrides/granitemoehybrid.py       |   23 +
 .../srt/arg_groups/model_overrides/inkling.py |   76 +
 .../model_overrides/interns2_mobius.py        |   20 +
 .../srt/arg_groups/model_overrides/kimi_k3.py |  191 ++
 .../srt/arg_groups/model_overrides/lfm2.py    |   20 +
 .../srt/arg_groups/model_overrides/llama4.py  |   47 +
 .../srt/arg_groups/model_overrides/mimo_v2.py |   37 +
 .../srt/arg_groups/model_overrides/minicpm.py |   66 +
 .../arg_groups/model_overrides/minicpmv.py    |   20 +
 .../arg_groups/model_overrides/minimax_m2.py  |   36 +
 .../arg_groups/model_overrides/minimax_m3.py  |  150 ++
 .../srt/arg_groups/model_overrides/moss_vl.py |   34 +
 .../model_overrides/muse_glimmer.py           |   24 +
 .../arg_groups/model_overrides/nemotron_h.py  |  123 ++
 .../srt/arg_groups/model_overrides/olmo2.py   |   34 +
 .../srt/arg_groups/model_overrides/qwen3_5.py |   54 +
 .../arg_groups/model_overrides/qwen3_moe.py   |   50 +
 .../arg_groups/model_overrides/qwen3_vl.py    |   32 +
 python/sglang/srt/arg_groups/overrides.py     | 1610 +----------------
 python/sglang/srt/arg_groups/serving_hook.py  |   29 +
 python/sglang/srt/entrypoints/http_server.py  |    5 +-
 .../srt/entrypoints/http_server_engine.py     |    5 +-
 .../load_model_utils.py                       |    8 +-
 python/sglang/srt/runtime_context.py          |    8 +-
 python/sglang/srt/server_args.py              |   86 +-
 .../unit/managers/test_mm_process_config.py   |   27 +-
 .../server_args/test_model_source_paths.py    |  326 ++++
 .../test_no_public_non_field_slot.py          |   13 +-
 .../test_record_member_calls_resolve.py       |  144 ++
 .../unit/server_args/test_server_args.py      |   16 +-
 .../unit/test_chain_read_ratchet.py           |   70 +-
 .../unit/test_model_override_split.py         |  120 ++
 test/registered/unit/test_model_overrides.py  |  187 +-
 test/registered/unit/test_runtime_context.py  |    5 +-
 44 files changed, 2898 insertions(+), 1730 deletions(-)
 create mode 100644 python/sglang/srt/arg_groups/model_override_base.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/__init__.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/deepseek_v2.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/deepseek_v4.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/exaone.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/falcon_h1.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/gemma2_gemma3.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/gemma4.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/glm4_moe.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/gpt_oss.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/granitemoehybrid.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/inkling.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/interns2_mobius.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/kimi_k3.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/lfm2.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/llama4.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/mimo_v2.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/minicpm.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/minicpmv.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/minimax_m2.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/minimax_m3.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/moss_vl.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/muse_glimmer.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/nemotron_h.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/olmo2.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/qwen3_5.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/qwen3_moe.py
 create mode 100644 python/sglang/srt/arg_groups/model_overrides/qwen3_vl.py
 create mode 100644 test/registered/unit/server_args/test_model_source_paths.py
 create mode 100644 test/registered/unit/server_args/test_record_member_calls_resolve.py
 create mode 100644 test/registered/unit/test_model_override_split.py

diff --git a/python/sglang/srt/arg_groups/model_override_base.py b/python/sglang/srt/arg_groups/model_override_base.py
new file mode 100644
index 000000000..58658476f
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_override_base.py
@@ -0,0 +1,376 @@
+"""What the per-model override declarations are written against.
+
+The declarations themselves live one directory down, in
+``arg_groups/model_overrides/``: one module per model family, mirroring the
+``models/`` naming. This module is what they all import -- the registry they
+register into, the read-only views they are handed, and the few accessors that
+answer questions about the model. It deliberately depends on nothing in
+``overrides.py``, so a family module never has to import its way back up.
+"""
+
+import logging
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+from sglang.srt.platforms import current_platform
+from sglang.srt.runtime_context import get_platform
+from sglang.srt.utils.common import is_mps, is_no_spec_infer_or_topk_one
+
+logger = logging.getLogger(__name__)
+
+
+# Constant per-architecture overrides (populated by the migration sweeps).
+MODEL_OVERRIDES: Dict[str, Dict[str, Any]] = {
+    # These models run in bfloat16 regardless of the requested dtype
+    # (faithful port of the legacy unconditional arch branch).
+    "MistralLarge3ForCausalLM": {"dtype": "bfloat16"},
+    "PixtralForConditionalGeneration": {"dtype": "bfloat16"},
+}
+
+
+# Derived per-architecture override providers, in registration order.
+_MODEL_OVERRIDE_FNS: Dict[str, List[Callable[..., dict]]] = {}
+
+
+# Predicate-keyed providers, in registration order — for legacy branches
+# matched by substring/predicate on the architecture string rather than an
+# exact name (e.g. '"Step3p5ForCausalLM" in model_arch').
+_PREDICATE_OVERRIDE_FNS: List[Tuple[Callable[[str], bool], Callable[..., dict]]] = []
+
+
+def register_model_override(architecture: str):
+    """Register a derived-override provider for ``architecture``.
+
+    The decorated callable receives ``(server_args, hf_config)``, must not
+    mutate either, and returns a ``{field: resolved_value}`` dict (possibly
+    empty when nothing applies). Providers needing derived model data beyond
+    the HF config go through ``model_config_of(server_args)`` (cached,
+    read-only) — never anything mutating.
+    """
+
+    def decorator(fn: Callable[..., dict]) -> Callable[..., dict]:
+        _MODEL_OVERRIDE_FNS.setdefault(architecture, []).append(fn)
+        return fn
+
+    return decorator
+
+
+def register_model_override_predicate(predicate: Callable[[str], bool]):
+    """Register a derived-override provider keyed by an architecture
+    predicate. Same callable contract as ``register_model_override``."""
+
+    def decorator(fn: Callable[..., dict]) -> Callable[..., dict]:
+        _PREDICATE_OVERRIDE_FNS.append((predicate, fn))
+        return fn
+
+    return decorator
+
+
+def _invoke_provider(
+    fn: Callable[..., dict], server_args: Any, hf_config: Any
+) -> Dict[str, Any]:
+    declared = fn(server_args, hf_config)
+    if not isinstance(declared, dict):
+        raise TypeError(
+            f"model override provider {fn.__qualname__} must return a dict, "
+            f"got {type(declared).__name__}"
+        )
+    return declared
+
+
+class ResolvedView:
+    """Read-only view of the resolving configuration handed to post-process
+    passes: the accumulated declarations overlaid on the pristine
+    ``server_args`` (residual imperative writes of non-resolved fields show
+    through the fallthrough) — exactly the state the legacy handler at the
+    same slot observed. Writes are rejected: passes return declarations.
+    """
+
+    __slots__ = ("_server_args", "_overlay")
+
+    def __init__(self, server_args: Any, overlay: Optional[Dict[str, Any]] = None):
+        object.__setattr__(self, "_server_args", server_args)
+        object.__setattr__(self, "_overlay", overlay or {})
+
+    def __getattr__(self, name: str) -> Any:
+        overlay = object.__getattribute__(self, "_overlay")
+        if name in overlay:
+            return overlay[name]
+        return getattr(object.__getattribute__(self, "_server_args"), name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        raise AttributeError(
+            "ResolvedView is read-only; post-process passes return declarations"
+        )
+
+
+class ResolvingConfig:
+    """Live read view of the resolution result: the declaration stash over the
+    record's fields, looked up per read.
+
+    ``ResolvedView`` snapshots the overlay when it is built, which is what a
+    post-process pass wants -- it reads the state at its slot. A resolver that
+    reads *after* declaring, or after calling something that declares, needs the
+    current answer instead, so this one walks the stash on every read. It falls
+    through to the field, which is where the raw input lives.
+    """
+
+    __slots__ = ("_server_args",)
+
+    def __init__(self, server_args: Any):
+        object.__setattr__(self, "_server_args", server_args)
+
+    def __getattr__(self, name: str) -> Any:
+        server_args = object.__getattribute__(self, "_server_args")
+        for _source, declared in reversed(
+            getattr(server_args, "_resolved_overrides", None) or ()
+        ):
+            if name in declared:
+                return declared[name]
+        return getattr(server_args, name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        raise AttributeError(
+            "ResolvingConfig is read-only; resolution writes through declarations"
+        )
+
+
+def resolving_view(server_args: Any) -> ResolvingConfig:
+    """A live read view of what resolution has decided so far."""
+    return ResolvingConfig(server_args)
+
+
+def _declaration_overlay(server_args: Any) -> Dict[str, Any]:
+    """What the declarations say so far, last writer wins.
+
+    Nothing writes the fields, so a mid-resolution reader needs this to see a
+    decision at all; the fields keep what the caller supplied."""
+    overlay: Dict[str, Any] = {}
+    for _source, declared in getattr(server_args, "_resolved_overrides", None) or ():
+        overlay.update(declared)
+    return overlay
+
+
+def resolved_view(server_args: Any) -> ResolvedView:
+    """Read-only view of the resolving configuration: the declarations
+    overlaid on the fields, snapshotted per call.
+
+    For mid-resolution code that is not a pass (``__post_init__`` handlers and
+    hooks) that must answer with what resolution decided -- a declaration-only resolver (a model-specific
+    override, a registry entry) never writes the field, so a field read there
+    answers with the raw input."""
+    return ResolvedView(server_args, overlay=_declaration_overlay(server_args))
+
+
+def attention_backends_of(cfg: Any) -> tuple:
+    """(prefill, decode) attention backends of a config-shaped object (a
+    ResolvedView mid-resolution, or pristine server_args at dispatch time):
+    split fields fall back to the base backend."""
+    prefill = (
+        cfg.prefill_attention_backend
+        if cfg.prefill_attention_backend
+        else cfg.attention_backend
+    )
+    decode = (
+        cfg.decode_attention_backend
+        if cfg.decode_attention_backend
+        else cfg.attention_backend
+    )
+    return prefill, decode
+
+
+def _register_for(*architectures: str):
+    """Register one provider for several architectures (family lists)."""
+
+    def decorator(fn: Callable[..., dict]) -> Callable[..., dict]:
+        for architecture in architectures:
+            register_model_override(architecture)(fn)
+        return fn
+
+    return decorator
+
+
+def record_of(view: Any) -> Any:
+    """The record a view reads through.
+
+    For the few helpers a view cannot serve: `get_default_attn_backend` reads
+    through *both* overlays, so it needs the record the two views are built
+    from rather than either one of them.
+    """
+    return object.__getattribute__(view, "_server_args")
+
+
+def is_attention_backend_not_set(cfg: Any):
+    """None of the three attention backends has been decided yet.
+
+    Takes the view rather than the record: every read is a view read, and the
+    callers that hold a view (the override providers) would otherwise have to
+    reach back through it for a record.
+    """
+    return (
+        cfg.attention_backend is None
+        and cfg.prefill_attention_backend is None
+        and cfg.decode_attention_backend is None
+    )
+
+
+def use_mla_backend(server_args: Any):
+    from sglang.srt.configs.model_config import AttentionArch
+
+    model_config = model_config_of(server_args)
+    return model_config.attention_arch == AttentionArch.MLA
+
+
+def model_config_of(server_args: Any):
+    """The model configuration this record describes, built once and memoised.
+
+    Takes a view as readily as the record: a view is a read overlay of one
+    record, the memo has to live on that record either way, and the callers
+    that hold a view would otherwise all have to unwrap it themselves.
+    """
+    if isinstance(server_args, (ResolvedView, ResolvingConfig)):
+        server_args = record_of(server_args)
+    # Lazy init to avoid circular import
+    cfg = resolving_view(server_args)
+    from sglang.srt.configs.model_config import ModelConfig
+
+    memo = getattr(server_args, "_model_config", None)
+    if memo is not None:
+        # The key is the path this record carried when the cache was
+        # filled. The GGUF and ModelScope handlers declare a different
+        # `model_path`, and a configuration built before them describes
+        # another checkpoint. `ModelConfig` re-points its own `model_path`
+        # at the local pull directory when the weights sit behind an
+        # object-store URI, so its field is not the key. A configuration a
+        # fixture supplied carries no key and is handed back as it is.
+        built_from = getattr(server_args, "_model_config_built_from", None)
+        if built_from is None or built_from == cfg.model_path:
+            return memo
+
+    model_config = ModelConfig.from_server_args(server_args)
+    server_args._model_config = model_config
+    server_args._model_config_built_from = cfg.model_path
+    if model_config.is_hybrid_swa:
+        logger.info(
+            "Hybrid SWA model detected. architectures=%s",
+            model_config.hf_config.architectures,
+        )
+    return model_config
+
+
+def mamba_extra_buffer_of(cfg: Any) -> bool:
+    """Mid-resolution equivalent of runtime_context.mamba_extra_buffer_enabled:
+    reads the (possibly overlaid) strategy from a config-shaped object.
+
+    This is the one definition of the predicate: ``ServerArgs`` delegates its
+    member to it, and the runtime_context accessor is its post-publish sibling
+    (which cannot reuse it, because the two leaves land in different bags)."""
+    return cfg.disable_radix_cache is False and cfg.mamba_radix_cache_strategy in (
+        "extra_buffer",
+        "extra_buffer_lazy",
+    )
+
+
+def get_default_attn_backend(server_args: Any, use_mla_backend: bool, model_config):
+    """
+    Auto select the fastest attention backend.
+
+    1. Models with MHA Architecture (e.g: Llama, QWen)
+        1.1 We will turn on FA3 on hopper unless user use spec decode with topk > 1 or page_size > 1.
+        1.2 Use trtllm_mha for SM100/SM103 (Blackwell B200/GB200/B300) excluding spec with topk > 1.
+           Note: trtllm_mha does not support SM120, which will fall back to flashinfer.
+        1.3 In other cases, we will use flashinfer if available, otherwise use triton.
+    2. Models with MLA Architecture and using FA3
+        2.1 We will use FA3 backend on hopper.
+        2.2 We will use Flashinfer backend on blackwell.
+        2.3 Otherwise, we will use triton backend.
+    """
+    cfg = resolving_view(server_args)
+    # OOT platforms provide their own default attention backend.
+    if current_platform.is_out_of_tree():
+        return current_platform.get_default_attention_backend()
+
+    # Whisper requires flashinfer for cross-attention CUDA graph support.
+    if "WhisperForConditionalGeneration" in (
+        model_config.hf_config.architectures or []
+    ):
+        return "flashinfer"
+
+    if not use_mla_backend:
+        # MHA architecture
+
+        if get_platform().is_hopper_with_cuda_12_3 and is_no_spec_infer_or_topk_one(
+            resolved_view(server_args)
+        ):
+            # Note: flashinfer 0.6.1 caused performance regression on Hopper attention kernel
+            # Before the kernel is fixed, we choose fa3 as the default backend on Hopper MHA
+            # ref: https://github.com/sgl-project/sglang/issues/17411
+            return "fa3"
+        elif (
+            get_platform().is_sm100
+            and is_no_spec_infer_or_topk_one(resolved_view(server_args))
+            and (
+                cfg.speculative_algorithm is None
+                or cfg.speculative_eagle_topk is not None
+            )
+        ):
+            # trtllm_mha requires equal K/V row widths; fa4 carries
+            # v_head_dim through.
+            if model_config.has_asymmetric_kv:
+                return "fa4"
+            return "trtllm_mha"
+        elif get_platform().is_hip:
+            return "aiter"
+        elif is_mps():
+            return "torch_native"
+        else:
+            # FlashInfer does not support attention sinks.
+            if get_platform().has_flashinfer and not model_config.has_attention_sinks:
+                return "flashinfer"
+            return "triton"
+    else:
+        # MLA architecture
+        if get_platform().is_hopper_with_cuda_12_3:
+            return "fa3"
+        elif get_platform().is_sm100:
+            return "flashinfer"
+        elif get_platform().is_hip:
+            head_num = model_config.get_num_kv_heads(cfg.tp_size)
+            # TODO current aiter only support head number 16 or 128 head number
+            if head_num == 128 or head_num == 16:
+                return "aiter"
+            else:
+                return "triton"
+        elif is_mps():
+            return "torch_native"
+        else:
+            return "triton"
+
+
+def _dspark_verify_on_decode_backend(
+    backend: Optional[str], q_len: int, kv_cache_dtype: Optional[str]
+) -> bool:
+    """Whether the MLA decode backend can serve a q_len-wide target verify."""
+    if backend == "trtllm_mla":
+        return True
+    if backend == "tokenspeed_mla":
+        return kv_cache_dtype == "fp8_e4m3" and q_len <= 8
+    if backend == "cutedsl_mla":
+        # cute-dsl monolithic MLA decode folds the verify tokens into the head
+        # dim (fold_sq), so it serves any DSPARK verify width. Needs flashinfer
+        # >= 0.6.15 (older builds reject q_len >= 5).
+        return True
+    return False
+
+
+def _is_mxfp4_pack_quantized(hf_config: Any) -> bool:
+    qc = getattr(
+        getattr(hf_config, "text_config", hf_config), "quantization_config", None
+    )
+    if not isinstance(qc, dict):
+        return False
+    groups = qc.get("config_groups") or {}
+    return any(
+        "mxfp4" in str(g.get("format", ""))
+        for g in groups.values()
+        if isinstance(g, dict)
+    )
diff --git a/python/sglang/srt/arg_groups/model_overrides/__init__.py b/python/sglang/srt/arg_groups/model_overrides/__init__.py
new file mode 100644
index 000000000..a9e536147
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/__init__.py
@@ -0,0 +1,37 @@
+"""Per-model config-time override declarations, one module per family,
+mirroring the ``models/`` naming.
+
+Importing this package is what registers them. An architecture may be claimed
+by more than one module here -- one supplies its attention shape, another its
+MoE runner -- but two of them must never declare the *same* field for it:
+nobody would own that value, and which module supplied it would come down to
+the order of the imports below. ``test_model_override_split.py`` forbids the
+overlap, which is why this list needs no particular order.
+"""
+
+from sglang.srt.arg_groups.model_overrides import deepseek_v2  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import deepseek_v4  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import exaone  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import falcon_h1  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import gemma2_gemma3  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import gemma4  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import glm4_moe  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import gpt_oss  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import granitemoehybrid  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import inkling  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import interns2_mobius  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import kimi_k3  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import lfm2  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import llama4  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import mimo_v2  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import minicpm  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import minicpmv  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import minimax_m2  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import minimax_m3  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import moss_vl  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import muse_glimmer  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import nemotron_h  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import olmo2  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import qwen3_5  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import qwen3_moe  # noqa: F401
+from sglang.srt.arg_groups.model_overrides import qwen3_vl  # noqa: F401
diff --git a/python/sglang/srt/arg_groups/model_overrides/deepseek_v2.py b/python/sglang/srt/arg_groups/model_overrides/deepseek_v2.py
new file mode 100644
index 000000000..e50d65be1
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/deepseek_v2.py
@@ -0,0 +1,145 @@
+"""Config-time override declarations for deepseek_v2.
+
+Architectures: DeepseekV32ForCausalLM, DeepseekV3ForCausalLM, Dots3NoteForCausalLM, GlmMoeDsaForCausalLM, KimiK25ForConditionalGeneration, LongcatFlashForCausalLM, LongcatFlashForCausalLMNextN, MistralLarge3ForCausalLM, PixtralForConditionalGeneration.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    resolving_view,
+    use_mla_backend,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for(
+    "DeepseekV3ForCausalLM",
+    "DeepseekV32ForCausalLM",
+    "KimiK25ForConditionalGeneration",
+    "MistralLarge3ForCausalLM",
+    "PixtralForConditionalGeneration",
+    "GlmMoeDsaForCausalLM",
+    "LongcatFlashForCausalLM",
+    "LongcatFlashForCausalLMNextN",
+    "Dots3NoteForCausalLM",
+)
+def _deepseek_family_overrides(server_args: Any, hf_config: Any) -> dict:
+    """Order-safe declarations of the DeepSeek/DSA branch. The CP parallel
+    writes (enable_dp_attention/ep_size/moe_a2a_backend have post-monolith
+    writers), the kv-cache/split-backend defaults, the quant/moe block (read
+    before it by _set_default_dsa_kv_cache_dtype) and the env writes stay in
+    the branch."""
+    cfg = resolving_view(server_args)
+    from sglang.srt.configs.model_config import is_deepseek_dsa
+
+    overrides: Dict[str, Any] = {}
+
+    if is_deepseek_dsa(hf_config):  # DeepSeek 3.2/GLM 5
+        # Set attention backend for DeepSeek
+        if is_attention_backend_not_set(cfg):
+            overrides["attention_backend"] = "dsa"
+            logger.info("Use dsa attention backend for DeepSeek with DSA.")
+        if not get_platform().is_npu and not get_platform().is_xpu:  # CUDA or ROCm GPU
+            if cfg.enable_prefill_cp:
+                logger.warning(
+                    "Context parallel feature is still under experiment. It has only been verified on Hopper platform."
+                )
+                overrides["enable_dp_attention"] = True
+                overrides["moe_dense_tp_size"] = 1
+                if cfg.cp_strategy == "zigzag":
+                    overrides["moe_a2a_backend"] = "deepep"
+                    overrides["ep_size"] = cfg.tp_size
+                    logger.warning(
+                        "zigzag DSA CP requires moe_dense_tp_size=1, "
+                        "moe_a2a_backend=deepep, ep_size=tp_size, batch_size=1."
+                    )
+                else:
+                    assert (
+                        cfg.dp_size == 1
+                    ), "interleave DSA CP does not support DP attention."
+                assert (
+                    cfg.tp_size <= 8
+                ), "Context parallel only supports single machine (tp_size <= 8). Cross-machine CP has precision issues."
+                # Note(kpham-sgl): Keep attn_tp_size == 1 under DSA CP.
+                # DSACPLayerCommunicator does not all-reduce attention-TP
+                # partial o_proj outputs before replicated dense FFNs.
+                attn_cp_size = cfg.tp_size // cfg.dp_size
+                overrides["attn_cp_size"] = attn_cp_size
+                logger.warning(
+                    "Enabled DSA context parallel: "
+                    f"strategy={cfg.cp_strategy}, dp_size={cfg.dp_size}, "
+                    f"moe_dense_tp_size={overrides['moe_dense_tp_size']}, "
+                    f"ep_size={overrides.get('ep_size', cfg.ep_size)}, tp_size={cfg.tp_size}, "
+                    f"attn_cp_size={attn_cp_size}, "
+                    f"kv_cache_dtype={cfg.kv_cache_dtype}, "
+                    f"moe_a2a_backend={overrides.get('moe_a2a_backend', cfg.moe_a2a_backend)}, "
+                    f"cuda_graph_config[prefill].backend=disabled"
+                )
+
+            # Deferred import to avoid a circular import at module-load
+            # time (dsa.utils imports the runtime-context accessors).
+            from sglang.srt.layers.attention.dsa.utils import (
+                aiter_can_use_preshuffle_paged_mqa,
+            )
+
+            if get_platform().is_hip and not aiter_can_use_preshuffle_paged_mqa():
+                # Legacy ROCm DSA path: aiter's gluon paged-MQA kernel is
+                # unavailable (Triton<3.5 and AITER_ENABLE_AOT_GLUON_PA_MQA_LOGITS
+                # not set, or SGLANG_DSA_HIP_DISABLE_PRESHUFFLE=1 / SGLANG_USE_AITER=0).
+                overrides["page_size"] = 1
+                logger.warning(
+                    "Setting page size to 1 for DeepSeek DSA on ROCm "
+                    "(aiter preshuffle paged-MQA path unavailable: "
+                    "needs Triton>=3.5.0 or AITER_ENABLE_AOT_GLUON_PA_MQA_LOGITS=1)."
+                )
+            else:
+                overrides["page_size"] = 64
+                logger.warning("Setting page size to 64 for DeepSeek DSA.")
+    else:
+        # DeepSeek V3/R1/V3.1
+        if get_platform().is_sm100:
+            if (
+                cfg.attention_backend is None
+                and cfg.prefill_attention_backend is None
+                and cfg.decode_attention_backend is None
+            ):
+                overrides["attention_backend"] = "trtllm_mla"
+                logger.info(
+                    "Use trtllm_mla as attention backend on sm100 for DeepseekV3ForCausalLM"
+                )
+        # MLA prefill CP auto-config. Mirrors the NSA CP block above
+        # (minus the in-seq/round-robin mode split, which MLA CP does not support)
+        if cfg.enable_prefill_cp and use_mla_backend(server_args):
+            logger.warning(
+                "MLA prefill context parallel is still experimental. "
+                "Verified on Hopper with the fa3 backend."
+            )
+            overrides["enable_dp_attention"] = True
+            # TODO(kpham-sgl) Supports moe_dense_tp_size != 1.
+            overrides["moe_dense_tp_size"] = 1
+            overrides["moe_a2a_backend"] = "deepep"
+            overrides["ep_size"] = cfg.tp_size
+            logger.warning(
+                "For MLA CP, we have the following restrictions: moe_dense_tp_size == 1, moe_a2a_backend == deepep, ep_size == tp_size, batch_size == 1"
+            )
+            # FIXME(kpham-sgl): Keep attn_tp_size == 1 under MLA CP.
+            # DSACPLayerCommunicator does not all-reduce attention-TP
+            # partial o_proj outputs before replicated dense FFNs.
+            attn_cp_size = cfg.tp_size // cfg.dp_size
+            overrides["attn_cp_size"] = attn_cp_size
+            logger.warning(
+                f"Enable Context Parallel opt for MLA, "
+                f"Setting dp_size == {cfg.dp_size} and "
+                f"attn_cp_size == {attn_cp_size}, "
+                f"moe_dense_tp_size == {overrides['moe_dense_tp_size']}, "
+                f"ep_size == {overrides['ep_size']}, "
+                f"tp_size == {cfg.tp_size}, "
+                f"moe_a2a_backend {overrides['moe_a2a_backend']}, "
+                f"cuda_graph_config[prefill].backend=disabled"
+            )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/deepseek_v4.py b/python/sglang/srt/arg_groups/model_overrides/deepseek_v4.py
new file mode 100644
index 000000000..558260893
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/deepseek_v4.py
@@ -0,0 +1,76 @@
+"""Config-time override declarations for deepseek_v4.
+
+Architectures: DeepseekV4ForCausalLM.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    model_config_of,
+    resolving_view,
+)
+from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("DeepseekV4ForCausalLM")
+def _deepseek_v4_overrides(server_args: Any, hf_config: Any) -> dict:
+    """DeepSeek V4 attention/page/window/MoE-runner defaults (from
+    arg_groups/deepseek_v4_hook.py). The kv-cache dtype and NPU split-backend
+    writes, the max_running_requests fill and the validations stay in the
+    hook at its legacy slot."""
+    cfg = resolving_view(server_args)
+    from sglang.srt.server_args import ServerArgs
+
+    model_arch = hf_config.architectures[0]
+    overrides: Dict[str, Any] = {"attention_backend": "dsv4"}
+
+    page_size = 256
+    if cfg.device == "npu":
+        # NPU keeps the device-aware "dsv4" backend (the registry routes it to
+        # the Ascend V4 subclass); only the pool geometry / dtype differ.
+        # set_default_server_args() pins all three backends to "ascend" for
+        # generic NPU models; override that here so V4 stays consistently on
+        # dsv4.
+        page_size = 128
+        overrides["prefill_attention_backend"] = "dsv4"
+        overrides["decode_attention_backend"] = "dsv4"
+    overrides["page_size"] = page_size
+    logger.info(
+        f"Use dsv4 attention backend for {model_arch}, setting page_size to {page_size}."
+    )
+
+    if cfg.swa_full_tokens_ratio == ServerArgs.swa_full_tokens_ratio:
+        overrides["swa_full_tokens_ratio"] = 0.1
+        logger.info(f"Setting swa_full_tokens_ratio to 0.1 for {model_arch}.")
+
+    if cfg.moe_runner_backend == "auto":
+        model_config = model_config_of(server_args)
+        # nvidia/DeepSeek-V4-Pro-NVFP4 uses the routed TRT-LLM runner.
+        if model_config.nvfp4_moe_meta is not None:
+            overrides["moe_runner_backend"] = "flashinfer_trtllm_routed"
+            logger.info(
+                "Use flashinfer_trtllm_routed as MoE runner backend for "
+                f"{model_arch} hybrid FP8+NVFP4 checkpoint."
+            )
+        elif (
+            cfg.device == "cuda"
+            and not get_platform().is_hip
+            and cfg.moe_a2a_backend == "none"
+            and not envs.SGLANG_DSV4_FP4_DEQUANT.get()
+            and model_config.is_fp4_experts
+            and (
+                get_platform().is_sm90
+                or get_platform().is_sm100
+                or get_platform().is_sm120
+            )
+        ):
+            overrides["moe_runner_backend"] = "flashinfer_mxfp4"
+            logger.info(
+                "Use flashinfer_mxfp4 as MoE runner backend for " f"{model_arch}."
+            )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/exaone.py b/python/sglang/srt/arg_groups/model_overrides/exaone.py
new file mode 100644
index 000000000..17095fd5e
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/exaone.py
@@ -0,0 +1,23 @@
+"""Config-time override declarations for exaone.
+
+Architectures: Exaone4ForCausalLM, ExaoneMoEForCausalLM.
+"""
+
+import logging
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("Exaone4ForCausalLM", "ExaoneMoEForCausalLM")
+def _exaone_overrides(server_args: Any, hf_config: Any) -> dict:
+    if hf_config.sliding_window_pattern is not None:
+        logger.warning(
+            f"Disabling hybrid SWA memory for {hf_config.architectures[0]} as it is not yet supported."
+        )
+        return {"disable_hybrid_swa_memory": True}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/falcon_h1.py b/python/sglang/srt/arg_groups/model_overrides/falcon_h1.py
new file mode 100644
index 000000000..3a55aac04
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/falcon_h1.py
@@ -0,0 +1,22 @@
+"""Config-time override declarations for falcon_h1.
+
+Architectures: FalconH1ForCausalLM, JetNemotronForCausalLM, JetVLMForConditionalGeneration.
+"""
+
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+
+@_register_for(
+    "FalconH1ForCausalLM", "JetNemotronForCausalLM", "JetVLMForConditionalGeneration"
+)
+def _falcon_h1_jet_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if get_platform().is_sm100 and cfg.attention_backend is None:
+        return {"attention_backend": "triton"}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/gemma2_gemma3.py b/python/sglang/srt/arg_groups/model_overrides/gemma2_gemma3.py
new file mode 100644
index 000000000..e7616205f
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/gemma2_gemma3.py
@@ -0,0 +1,29 @@
+"""Config-time override declarations for gemma2_gemma3.
+
+Architectures: Gemma2ForCausalLM, Gemma3ForCausalLM, Gemma3ForConditionalGeneration, Gemma3nForCausalLM, Gemma3nForConditionalGeneration.
+"""
+
+import logging
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for(
+    "Gemma2ForCausalLM",
+    "Gemma3ForCausalLM",
+    "Gemma3ForConditionalGeneration",
+    "Gemma3nForCausalLM",
+    "Gemma3nForConditionalGeneration",
+)
+def _gemma2_gemma3_overrides(server_args: Any, hf_config: Any) -> dict:
+    # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with gemma2 model.
+    # It failed at this test: https://github.com/sgl-project/sglang/actions/runs/16255155597/job/45890331952#step:4:736
+    logger.warning(
+        f"Disable hybrid SWA memory for {hf_config.architectures[0]} as it is not yet supported."
+    )
+    return {"disable_hybrid_swa_memory": True}
diff --git a/python/sglang/srt/arg_groups/model_overrides/gemma4.py b/python/sglang/srt/arg_groups/model_overrides/gemma4.py
new file mode 100644
index 000000000..a3b496114
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/gemma4.py
@@ -0,0 +1,47 @@
+"""Config-time override declarations for gemma4.
+
+Architectures: Gemma4ForCausalLM, Gemma4ForConditionalGeneration, Gemma4UnifiedForConditionalGeneration.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    model_config_of,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for(
+    "Gemma4ForConditionalGeneration",
+    "Gemma4ForCausalLM",
+    "Gemma4UnifiedForConditionalGeneration",
+)
+def _gemma4_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+    default_attention_backend = "trtllm_mha" if get_platform().is_sm100 else "triton"
+    if is_attention_backend_not_set(cfg):
+        logger.info(
+            f"Use {default_attention_backend} as default attention backend for Gemma4"
+        )
+        overrides["attention_backend"] = default_attention_backend
+    # If only one split backend is set, keep the other side on a
+    # Gemma4-compatible fallback instead of letting generic backend selection
+    # choose an unsupported backend later.
+    elif cfg.attention_backend is None:
+        overrides["attention_backend"] = default_attention_backend
+    if get_platform().is_sm100 and cfg.moe_runner_backend == "auto":
+        if model_config_of(server_args).quantization == "modelopt_fp4":
+            overrides["quantization"] = "modelopt_fp4"
+            overrides["moe_runner_backend"] = "flashinfer_trtllm"
+            logger.info(
+                "Use flashinfer_trtllm as MoE runner backend on "
+                "SM100 for Gemma-4 (modelopt_fp4)"
+            )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/glm4_moe.py b/python/sglang/srt/arg_groups/model_overrides/glm4_moe.py
new file mode 100644
index 000000000..ebf50fa4f
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/glm4_moe.py
@@ -0,0 +1,50 @@
+"""Config-time override declarations for glm4_moe.
+
+Architectures: Glm4MoeForCausalLM.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("Glm4MoeForCausalLM")
+def _glm4_moe_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+    if get_platform().is_sm100:
+        quantization_config = getattr(hf_config, "quantization_config", None)
+        quant_method = (
+            quantization_config.get("quant_method")
+            if quantization_config is not None
+            else None
+        )
+        quantization = cfg.quantization
+        if (
+            quantization is None
+            and not server_args._quantization_explicitly_unset
+            and quant_method is not None
+        ):
+            overrides["quantization"] = quant_method
+            quantization = quant_method
+        if (
+            quantization in {"modelopt_fp4", None}
+            and cfg.moe_a2a_backend == "none"
+            and cfg.moe_runner_backend == "auto"
+        ):
+            overrides["moe_runner_backend"] = "flashinfer_trtllm"
+            logger.info(
+                "Use flashinfer_trtllm as MoE runner backend on sm100 for Glm4MoeForCausalLM"
+            )
+    logger.info(
+        "Enable TF32 matmul for Glm4MoeForCausalLM model to improve gate gemm performance."
+    )
+    overrides["enable_tf32_matmul"] = True
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/gpt_oss.py b/python/sglang/srt/arg_groups/model_overrides/gpt_oss.py
new file mode 100644
index 000000000..447702749
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/gpt_oss.py
@@ -0,0 +1,127 @@
+"""Config-time override declarations for gpt_oss.
+
+Architectures: GptOssForCausalLM.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    resolving_view,
+)
+from sglang.srt.environ import envs
+from sglang.srt.hardware_backend.mlx.runtime import use_mlx
+from sglang.srt.runtime_context import get_platform
+from sglang.srt.utils.common import (
+    get_nvidia_driver_version,
+    is_cpu,
+    is_mps,
+    is_triton_kernels_available,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("GptOssForCausalLM")
+def _gpt_oss_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+    # Set attention backend for GPT-OSS
+    if is_attention_backend_not_set(cfg):
+        if get_platform().is_sm100:
+            overrides["attention_backend"] = "trtllm_mha"
+        elif get_platform().is_sm90:
+            overrides["attention_backend"] = "fa3"
+        elif is_cpu() and get_platform().has_amx:
+            overrides["attention_backend"] = "intel_amx"
+        elif get_platform().is_xpu:
+            overrides["attention_backend"] = "intel_xpu"
+        elif get_platform().is_hip:
+            overrides["attention_backend"] = "aiter"
+        elif not (is_mps() and use_mlx()):
+            # Exempt MLX only -- it owns attention in its own runner.  macOS
+            # without MLX still falls through to triton and fails fast below,
+            # rather than landing on torch_native (no sliding window, no sinks).
+            overrides["attention_backend"] = "triton"
+    if get_platform().is_xpu:
+        # Check for bf16 dtype on Intel XPU. Reads the pristine dtype request,
+        # which equals the legacy mid-branch read: dtype had no earlier writer
+        # for this arch.
+        if cfg.dtype == "auto":
+            logger.warning(
+                "GptOssForCausalLM on Intel XPU currently supports bfloat16 dtype only"
+            )
+        elif cfg.dtype not in ["bfloat16"]:
+            raise NotImplementedError(
+                f"GptOssForCausalLM on Intel XPU only supports bfloat16 dtype, "
+                f"but got '{cfg.dtype}'. Please use --dtype bfloat16 or remove --dtype to use auto."
+            )
+    quantization_config = getattr(hf_config, "quantization_config", None)
+    is_mxfp4_quant_format = (
+        quantization_config is not None
+        and quantization_config.get("quant_method") == "mxfp4"
+    )
+    if is_mxfp4_quant_format:
+        # use bf16 for mxfp4 triton kernels
+        overrides["dtype"] = "bfloat16"
+    if cfg.moe_runner_backend == "auto":
+
+        if get_platform().is_sm100 and is_mxfp4_quant_format:
+            overrides["moe_runner_backend"] = "flashinfer_mxfp4"
+            logger.warning(
+                "Detected SM100 and MXFP4 quantization format for GPT-OSS model, enabling FlashInfer MXFP4 MOE kernel."
+            )
+        elif get_platform().is_sm120 and is_mxfp4_quant_format:
+            overrides["moe_runner_backend"] = "flashinfer_mxfp4"
+            logger.warning(
+                "Detected SM120 and MXFP4 quantization format for GPT-OSS model, "
+                "enabling FlashInfer CUTLASS MXFP4 MOE kernel."
+            )
+        elif (
+            get_platform().is_hip and envs.SGLANG_USE_AITER.get()
+        ) and is_mxfp4_quant_format:
+            overrides["moe_runner_backend"] = "auto"
+            logger.warning(
+                "Detected ROCm and MXFP4 quantization format for GPT-OSS model, enabling aiter MXFP4 MOE kernel."
+            )
+            ## The AITER MXFP4 fused-MoE path for GPT-OSS expects the
+            ## SEPARATED gate/up tile layout (matches the
+            ## `gptoss_fp4_tuned_fmoe.csv` flydsl entries and the
+            ## Mxfp4MoEMethod weight shuffle). Other AITER MXFP4
+            ## callers default to INTERLEAVE; opt this path out
+            ## unless the user explicitly overrode it.
+            # envs.SGLANG_USE_AITER_MOE_GU_ITLV.set(False)
+        elif get_platform().is_hip and envs.SGLANG_USE_AITER.get():
+            # For GPT-OSS bf16 on ROCm with aiter, use triton backend
+            # because aiter CK kernel doesn't support all GEMM dimensions
+            overrides["moe_runner_backend"] = "triton"
+            logger.warning(
+                "Detected ROCm with SGLANG_USE_AITER for GPT-OSS bf16 model, using triton MOE kernel."
+            )
+        elif get_platform().is_musa and envs.SGLANG_DEEPEP_BF16_DISPATCH.get():
+            overrides["moe_runner_backend"] = "deep_gemm"
+            logger.warning(
+                "Detected MUSA with SGLANG_DEEPEP_BF16_DISPATCH for bf16 model, using deep_gemm kernel."
+            )
+        elif (
+            cfg.ep_size == 1
+            and is_triton_kernels_available()
+            and cfg.quantization is None
+            and not (is_cpu() and get_platform().has_amx)
+        ):
+            # The triton_kernels package segfaults on Blackwell (B200)
+            # with NVIDIA driver >= 595. Fall back to triton backend.
+            if get_platform().is_blackwell and get_nvidia_driver_version() >= (595,):
+                overrides["moe_runner_backend"] = "triton"
+                logger.warning(
+                    "Detected GPT-OSS model on Blackwell with driver >= 595, "
+                    "using triton MOE kernel to avoid triton_kernels SIGSEGV."
+                )
+            else:
+                overrides["moe_runner_backend"] = "triton_kernel"
+                logger.warning(
+                    "Detected GPT-OSS model, enabling triton_kernels MOE kernel."
+                )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/granitemoehybrid.py b/python/sglang/srt/arg_groups/model_overrides/granitemoehybrid.py
new file mode 100644
index 000000000..29ceb5f85
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/granitemoehybrid.py
@@ -0,0 +1,23 @@
+"""Config-time override declarations for granitemoehybrid.
+
+Architectures: GraniteMoeHybridForCausalLM.
+"""
+
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+
+@_register_for("GraniteMoeHybridForCausalLM")
+def _granite_moe_hybrid_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    has_mamba = any(
+        layer_type == "mamba" for layer_type in getattr(hf_config, "layer_types", [])
+    )
+    if has_mamba and get_platform().is_sm100 and cfg.attention_backend is None:
+        return {"attention_backend": "flashinfer"}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/inkling.py b/python/sglang/srt/arg_groups/model_overrides/inkling.py
new file mode 100644
index 000000000..41a3482e0
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/inkling.py
@@ -0,0 +1,76 @@
+"""Config-time override declarations for inkling.
+
+Architectures: InklingForConditionalGeneration, InklingForConditionalGenerationMTP.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    resolving_view,
+)
+from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for(
+    "InklingForConditionalGeneration",
+    "InklingForConditionalGenerationMTP",
+)
+def _inkling_overrides(server_args: Any, hf_config: Any) -> dict:
+    """Inkling architecture defaults: SWA / mamba KV-pool ratios tuned for the
+    hybrid-SWA layout, the extra-buffer mamba strategy, and the unified radix
+    tree (which Inkling requires — models/inkling.py asserts it). The full-graph
+    prefill default is set separately (inline, before cuda-graph resolution) —
+    see ServerArgs.__post_init__ / _apply_inkling_prefill_cuda_graph_default. The
+    server-arg defaults each yield to an explicit user value (compared against
+    the ServerArgs class default); the prefill declaration is materialized
+    before _parse_cuda_graph_config folds cuda_graph_backend_prefill into
+    prefill.backend, and an explicit --cuda-graph-backend-prefill /
+    --disable-prefill-cuda-graph still wins. The unified-radix env write follows
+    the MiniMax-M3 handler precedent (env is not a resolvable server-arg)."""
+    cfg = resolving_view(server_args)
+    from sglang.srt.server_args import ServerArgs
+
+    overrides: Dict[str, Any] = {}
+    # NOTE: the full-graph prefill default is NOT set here. cuda-graph config is
+    # resolved in __post_init__ before declarations are materialized, so a
+    # cuda_graph_backend_prefill declared here lands too late (the breakable
+    # default would already have been auto-disabled for this multimodal arch).
+    # It is set inline before _handle_cuda_graph_config instead.
+    if cfg.swa_full_tokens_ratio == ServerArgs.swa_full_tokens_ratio:
+        overrides["swa_full_tokens_ratio"] = 0.1
+    if cfg.mamba_full_memory_ratio == ServerArgs.mamba_full_memory_ratio:
+        overrides["mamba_full_memory_ratio"] = 0.1
+    # Inkling requires the extra-buffer mamba strategy (inkling.py asserts
+    # enable_mamba_extra_buffer()); the generic "auto" resolution does not cover
+    # Inkling, so pin it here. Yields to an explicit --mamba-scheduler-strategy.
+    #
+    # The default comparison answers "unset" only while nothing has declared the
+    # field first. `_mamba_radix_cache_resolution` would, from the slot just
+    # above `collect_model_override_declarations`, for an architecture whose
+    # linear-attention spec sets `uses_mamba_radix_cache`. Inkling has no such
+    # spec; giving it one silently stops this pin from firing, so compare
+    # against the unresolved token ("auto") if that day comes.
+    if cfg.mamba_radix_cache_strategy == ServerArgs.mamba_radix_cache_strategy:
+        overrides["mamba_radix_cache_strategy"] = "extra_buffer"
+    # Inkling attention runs only on the fa4 (Blackwell) or triton backends --
+    # models/inkling_common/attn.py asserts attention_backend in {fa4, triton}.
+    # The generic resolver would otherwise pick trtllm_mha (SM100) / fa3
+    # (Hopper), so a bare launch fails on the first attention forward. Pin a
+    # supported default when the user left every attention-backend flag unset
+    # (mirrors the MiniMax-M3 SM100 fa4-default above); an explicit
+    # --attention-backend / --prefill/decode-attention-backend still wins.
+    if is_attention_backend_not_set(cfg):
+        inkling_attn_backend = "fa4" if get_platform().is_sm100 else "triton"
+        overrides["attention_backend"] = inkling_attn_backend
+        logger.info(
+            f"Use {inkling_attn_backend} as the attention backend for Inkling "
+            "(requires fa4 or triton)."
+        )
+    envs.SGLANG_ENABLE_UNIFIED_RADIX_TREE.set(True)
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/interns2_mobius.py b/python/sglang/srt/arg_groups/model_overrides/interns2_mobius.py
new file mode 100644
index 000000000..bb11a3b0b
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/interns2_mobius.py
@@ -0,0 +1,20 @@
+"""Config-time override declarations for interns2_mobius.
+
+Architectures: InternS2MobiusForConditionalGeneration.
+"""
+
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+
+
+@_register_for("InternS2MobiusForConditionalGeneration")
+def _interns2_mobius_baseline_overrides(server_args: Any, hf_config: Any) -> dict:
+    """Select the only MoE runner validated for the 2,560-expert baseline."""
+    cfg = resolving_view(server_args)
+    if cfg.moe_runner_backend == "auto":
+        return {"moe_runner_backend": "triton_kernel"}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/kimi_k3.py b/python/sglang/srt/arg_groups/model_overrides/kimi_k3.py
new file mode 100644
index 000000000..d1579a31a
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/kimi_k3.py
@@ -0,0 +1,191 @@
+"""Config-time override declarations for kimi_k3.
+
+Architectures: KimiK3ForConditionalGeneration.
+"""
+
+import inspect
+import logging
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _dspark_verify_on_decode_backend,
+    _is_mxfp4_pack_quantized,
+    _register_for,
+    attention_backends_of,
+    is_attention_backend_not_set,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+from sglang.srt.utils.common import get_device_name, is_mnnvl_fabric_device
+
+logger = logging.getLogger(__name__)
+
+
+def _require_kimi_k3_cutedsl_dcp_support() -> None:
+    try:
+        from flashinfer.decode import trtllm_batch_decode_with_kv_cache_mla
+
+        parameters = inspect.signature(trtllm_batch_decode_with_kv_cache_mla).parameters
+    except (ImportError, TypeError, ValueError) as exc:
+        raise RuntimeError(
+            "Kimi-K3 DCP with decode_attention_backend='cutedsl_mla' requires "
+            "FlashInfer 0.6.17 or newer with "
+            "trtllm_batch_decode_with_kv_cache_mla exposing enable_dcp."
+        ) from exc
+
+    if "enable_dcp" not in parameters:
+        raise RuntimeError(
+            "Kimi-K3 DCP with decode_attention_backend='cutedsl_mla' requires "
+            "enable_dcp in the signature of "
+            "flashinfer.decode.trtllm_batch_decode_with_kv_cache_mla; upgrade "
+            "to FlashInfer 0.6.17 or newer."
+        )
+
+
+@_register_for("KimiK3ForConditionalGeneration")
+def _kimi_k3_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if cfg.dcp_size > 1:
+        overrides = {}
+        if cfg.enable_symm_mem:
+            logger.warning(
+                "Kimi-K3 DCP disables --enable-symm-mem due to decode CUDA "
+                "graph correctness issues."
+            )
+            overrides["enable_symm_mem"] = False
+
+        if cfg.speculative_algorithm == "DSPARK":
+            from sglang.srt.speculative.ragged_verify import (
+                RaggedVerifyMode,
+                read_ragged_verify_mode,
+            )
+
+            ragged_mode = read_ragged_verify_mode()
+            if ragged_mode is not RaggedVerifyMode.STATIC:
+                raise ValueError(
+                    "Kimi-K3 DCP + DSPARK currently requires "
+                    "SGLANG_RAGGED_VERIFY_MODE=static; compact/cap-accept are "
+                    f"not validated under DCP (got {ragged_mode.value!r})."
+                )
+
+            # DSPARK target-verify + draft-extend must run on the decode
+            # (cutedsl_mla) backend, whose _run_decode_kernel implements the DCP
+            # signature (causal_seqs / cp_world / cp_rank). The default
+            # "prefill" routes verify to trtllm_mla, whose base _run_decode_kernel
+            # lacks that DCP path (TypeError: unexpected kwarg 'causal_seqs').
+            overrides["speculative_attention_mode"] = "decode"
+
+        prefill_backend, decode_backend = attention_backends_of(cfg)
+        if decode_backend == "cutedsl_mla" or decode_backend is None:
+            _require_kimi_k3_cutedsl_dcp_support()
+            logger.info(
+                "Kimi-K3 DCP keeps decode attention backend 'cutedsl_mla' "
+                f"(prefill={prefill_backend!r} -> 'trtllm_mla')."
+            )
+            overrides.update(
+                prefill_attention_backend="trtllm_mla",
+                decode_attention_backend="cutedsl_mla",
+            )
+        elif decode_backend == "tokenspeed_mla":
+            logger.info(
+                "Kimi-K3 DCP overrides attention backends: "
+                f"prefill={prefill_backend!r}, decode={decode_backend!r} -> "
+                "'tokenspeed_mla'."
+            )
+            logger.info(
+                "Kimi-K3 DCP with tokenspeed mla backend overrides KV cache dtype: "
+                f"{cfg.kv_cache_dtype!r} -> 'fp8_e4m3'."
+            )
+            overrides.update(
+                prefill_attention_backend="tokenspeed_mla",
+                decode_attention_backend="tokenspeed_mla",
+                kv_cache_dtype="fp8_e4m3",
+            )
+        else:
+            raise AssertionError(
+                f"Decode attention backend for Kimi-K3 DCP must be 'cutedsl_mla' or 'tokenspeed_mla', got {decode_backend!r}."
+            )
+
+        if cfg.dcp_replicate_q_proj is None:
+            logger.info("Kimi-K3 DCP enables replicated Q projection by default.")
+            overrides["dcp_replicate_q_proj"] = True
+
+        device_name = get_device_name()
+        dcp_comm_backend = "fi_a2a" if is_mnnvl_fabric_device() else "a2a"
+        logger.info(
+            "Kimi-K3 DCP selects communication backend on "
+            f"{device_name!r}: {cfg.dcp_comm_backend!r} -> "
+            f"{dcp_comm_backend!r}."
+        )
+        overrides["dcp_comm_backend"] = dcp_comm_backend
+        return overrides
+
+    if not (get_platform().is_sm100 and get_platform().device_sm in (100, 103)):
+        return {}
+    backends_unset = is_attention_backend_not_set(cfg)
+    if cfg.speculative_algorithm != "DSPARK":
+        if not backends_unset:
+            return {}
+        logger.info(
+            "Use trtllm_mla as the default prefill and decode attention "
+            "backend for Kimi-K3 on SM100/SM103."
+        )
+        return {
+            "decode_attention_backend": "trtllm_mla",
+            "prefill_attention_backend": "trtllm_mla",
+        }
+    # DSPARK: verify runs on the decode backend (mode=decode below), so this
+    # picks the verify kernel -- mode=prefill routes it to flashinfer, which is
+    # slow and syncs, while plain decode is cold under dspark.
+    q_len = cfg.speculative_num_draft_tokens or (
+        cfg.speculative_dspark_block_size + 1
+        if cfg.speculative_dspark_block_size is not None
+        # Checkpoint auto-infer happens after overrides; K3 draft uses block 7.
+        else 8
+    )
+    overrides = {}
+    if backends_unset:
+        backend = "trtllm_mla"
+        overrides["decode_attention_backend"] = backend
+        overrides["prefill_attention_backend"] = "trtllm_mla"
+    else:
+        # Explicit backend knobs keep priority, but the mode is a separate knob
+        # that still needs declaring -- else verify stays on the prefill backend,
+        # whose host-side plan (flashinfer by default) forces a per-step D2H.
+        _, backend = attention_backends_of(cfg)
+    if _dspark_verify_on_decode_backend(backend, q_len, cfg.kv_cache_dtype):
+        overrides["speculative_attention_mode"] = "decode"
+        logger.info(
+            "Kimi-K3 DSPARK on SM100/SM103: decode/verify attention backend "
+            f"{backend} (speculative_attention_mode=decode)."
+        )
+    else:
+        logger.warning(
+            f"Kimi-K3 DSPARK: decode attention backend {backend!r} cannot serve "
+            f"target verify at q_len={q_len}, so verify runs on the prefill "
+            "backend (speculative_attention_mode=prefill). A host-plan prefill "
+            "backend costs a per-step seq_lens D2H sync; leave the attention "
+            "backend knobs unset for the sync-free default."
+        )
+    return overrides
+
+
+@_register_for("KimiK3ForConditionalGeneration")
+def _kimi_k3_moe_runner_overrides(server_args: Any, hf_config: Any) -> dict:
+    # MoE runner default, independent of the attention-backend gate above.
+    # trtllm-gen fused MoE (flashinfer_mxfp4) beats marlin on both the decode
+    # (M=bs) and the target-verify (M=bs*(gamma+1)) regimes on SM100/SM103.
+    # SM107 uses the same packed-MXFP4 runner; leaving auto unresolved falls
+    # back to BF16 weight materialization during model loading.
+    cfg = resolving_view(server_args)
+    if cfg.moe_runner_backend != "auto":
+        return {}
+    if not (get_platform().is_sm100 and get_platform().device_sm in (100, 103, 107)):
+        return {}
+    if not _is_mxfp4_pack_quantized(hf_config):
+        return {}
+    logger.info(
+        "Kimi-K3 on SM100/SM103/SM107: moe_runner_backend=flashinfer_mxfp4 "
+        "(FlashInfer SiTU kernels)."
+    )
+    return {"moe_runner_backend": "flashinfer_mxfp4"}
diff --git a/python/sglang/srt/arg_groups/model_overrides/lfm2.py b/python/sglang/srt/arg_groups/model_overrides/lfm2.py
new file mode 100644
index 000000000..8dc72ae9e
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/lfm2.py
@@ -0,0 +1,20 @@
+"""Config-time override declarations for lfm2.
+
+Architectures: Lfm2ForCausalLM, Lfm2MoeForCausalLM.
+"""
+
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+
+@_register_for("Lfm2ForCausalLM", "Lfm2MoeForCausalLM")
+def _lfm2_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if get_platform().is_sm100 and cfg.attention_backend is None:
+        return {"attention_backend": "flashinfer"}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/llama4.py b/python/sglang/srt/arg_groups/model_overrides/llama4.py
new file mode 100644
index 000000000..d6eff4f26
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/llama4.py
@@ -0,0 +1,47 @@
+"""Config-time override declarations for llama4.
+
+Architectures: Llama4ForCausalLM, Llama4ForConditionalGeneration.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+# Keep in sync with LLAMA4_MODEL_ARCHS (server_args.py).
+@_register_for("Llama4ForConditionalGeneration", "Llama4ForCausalLM")
+def _llama4_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if cfg.device == "cpu":
+        return {}
+    overrides: Dict[str, Any] = {}
+    # Auto-select attention backend for Llama4 if not specified
+    if cfg.attention_backend is None:
+        if get_platform().is_sm100:
+            backend, platform = "trtllm_mha", "sm100"
+        elif get_platform().is_sm90:
+            backend, platform = "fa3", "sm90"
+        elif get_platform().is_hip:
+            backend, platform = "aiter", "hip"
+        elif cfg.device == "xpu":
+            backend, platform = "intel_xpu", "xpu"
+        else:
+            backend, platform = "triton", "other platforms"
+        logger.warning(
+            f"Use {backend} as attention backend on {platform} for Llama4 model"
+        )
+        overrides["attention_backend"] = backend
+    if get_platform().is_sm100 and cfg.moe_runner_backend == "auto":
+        if cfg.quantization in {"fp8", "modelopt_fp8"}:
+            overrides["moe_runner_backend"] = "flashinfer_trtllm"
+            logger.info(
+                "Use flashinfer_trtllm as MoE runner backend on SM100 for Llama4"
+            )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/mimo_v2.py b/python/sglang/srt/arg_groups/model_overrides/mimo_v2.py
new file mode 100644
index 000000000..bef9a3630
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/mimo_v2.py
@@ -0,0 +1,37 @@
+"""Config-time override declarations for mimo_v2.
+
+Architectures: MiMoV2FlashForCausalLM, MiMoV2ForCausalLM.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+from sglang.srt.utils.common import get_quantization_config
+
+logger = logging.getLogger(__name__)
+
+
+# Keep in sync with MIMO_V2_MODEL_ARCHS (server_args.py / configs/hf_config.py).
+@_register_for("MiMoV2ForCausalLM", "MiMoV2FlashForCausalLM")
+def _mimo_v2_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+    if cfg.speculative_algorithm == "EAGLE":
+        logger.info("Enable multi-layer EAGLE speculative decoding for MiMoV2 model.")
+        overrides["enable_multi_layer_eagle"] = True
+
+    # On Blackwell "auto" falls through to the triton fused-MoE runner, ~12%
+    # slower at bs=1 decode. FP4 checkpoints use flashinfer_mxfp4 instead.
+    if (
+        get_platform().is_sm100
+        and cfg.moe_runner_backend == "auto"
+        and get_quantization_config(hf_config) == "fp8"
+    ):
+        overrides["moe_runner_backend"] = "flashinfer_trtllm"
+        logger.info("MiMoV2 FP8 on SM100: moe_runner_backend=flashinfer_trtllm.")
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/minicpm.py b/python/sglang/srt/arg_groups/model_overrides/minicpm.py
new file mode 100644
index 000000000..76fb4fef5
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/minicpm.py
@@ -0,0 +1,66 @@
+"""Config-time override declarations for minicpm.
+
+Architectures: MiniCPMForCausalLM, MiniCPMSALAForCausalLM.
+"""
+
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    resolving_view,
+)
+from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_platform
+
+
+@_register_for("MiniCPMForCausalLM", "MiniCPMSALAForCausalLM")
+def _minicpm_sala_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if cfg.enable_dp_attention:
+        raise ValueError("MiniCPM does not support DP attention")
+    has_sparse_attention = getattr(hf_config, "has_minicpm_sparse_attention", False)
+    has_hybrid_attention = has_sparse_attention or getattr(
+        hf_config, "has_lightning_layers", False
+    )
+    overrides: Dict[str, Any] = {}
+    if has_hybrid_attention:
+        if cfg.enable_hierarchical_cache:
+            raise ValueError("MiniCPM SALA does not support hierarchical cache")
+        overrides["disable_radix_cache"] = True
+    if envs.SGLANG_MINICPM_FORCE_DENSE.get():
+        dense_backends = {
+            "minicpm_flashattn": ("fa4" if get_platform().is_blackwell else "fa3"),
+            "minicpm_flashinfer": "flashinfer",
+        }
+        # Literal keys keep the written-field set statically derivable; a loop
+        # variable hides it from the census in test_chain_read_ratchet.py.
+        dense_attention = dense_backends.get(cfg.attention_backend)
+        if dense_attention is not None:
+            overrides["attention_backend"] = dense_attention
+        dense_prefill = dense_backends.get(cfg.prefill_attention_backend)
+        if dense_prefill is not None:
+            overrides["prefill_attention_backend"] = dense_prefill
+        dense_decode = dense_backends.get(cfg.decode_attention_backend)
+        if dense_decode is not None:
+            overrides["decode_attention_backend"] = dense_decode
+    elif has_sparse_attention:
+        uses_sparse_backend = is_attention_backend_not_set(cfg) or any(
+            backend in ("minicpm_flashattn", "minicpm_flashinfer")
+            for backend in (
+                cfg.attention_backend,
+                cfg.prefill_attention_backend,
+                cfg.decode_attention_backend,
+            )
+        )
+        if uses_sparse_backend and cfg.disaggregation_mode != "null":
+            raise ValueError(
+                "MiniCPM sparse attention does not support PD disaggregation"
+            )
+        if is_attention_backend_not_set(cfg):
+            overrides["attention_backend"] = (
+                "minicpm_flashinfer"
+                if get_platform().is_blackwell
+                else "minicpm_flashattn"
+            )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/minicpmv.py b/python/sglang/srt/arg_groups/model_overrides/minicpmv.py
new file mode 100644
index 000000000..c4c79fea6
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/minicpmv.py
@@ -0,0 +1,20 @@
+"""Config-time override declarations for minicpmv.
+
+Architectures: MiniCPMV4_6ForConditionalGeneration.
+"""
+
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+
+@_register_for("MiniCPMV4_6ForConditionalGeneration")
+def _minicpm_v4_6_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if get_platform().is_sm100 and cfg.attention_backend is None:
+        return {"attention_backend": "triton"}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/minimax_m2.py b/python/sglang/srt/arg_groups/model_overrides/minimax_m2.py
new file mode 100644
index 000000000..f9e966458
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/minimax_m2.py
@@ -0,0 +1,36 @@
+"""Config-time override declarations for minimax_m2.
+
+Architectures: MiniMaxM2ForCausalLM.
+"""
+
+import logging
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    model_config_of,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("MiniMaxM2ForCausalLM")
+def _minimax_m2_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides = {"enable_tf32_matmul": True}
+    logger.info(
+        "Enable TF32 matmul for MiniMaxM2ForCausalLM model to improve gate gemm performance."
+    )
+    if (
+        get_platform().is_sm100
+        and cfg.moe_runner_backend == "auto"
+        and model_config_of(server_args).quantization == "modelopt_fp4"
+    ):
+        overrides["moe_runner_backend"] = "flashinfer_trtllm_routed"
+        logger.info(
+            "Use flashinfer_trtllm_routed as MoE runner backend on SM10X "
+            "for MiniMaxM2ForCausalLM with modelopt_fp4."
+        )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/minimax_m3.py b/python/sglang/srt/arg_groups/model_overrides/minimax_m3.py
new file mode 100644
index 000000000..dbb17ae18
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/minimax_m3.py
@@ -0,0 +1,150 @@
+"""Config-time override declarations for minimax_m3.
+
+Architectures: MiniMaxM3SparseForCausalLM, MiniMaxM3SparseForConditionalGeneration.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    resolving_view,
+)
+from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_platform
+from sglang.srt.utils.common import get_quantization_config
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")
+def _minimax_m3_overrides(server_args: Any, hf_config: Any) -> dict:
+
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+
+    quant_method = get_quantization_config(hf_config)
+    quant_resolved = cfg.quantization
+    if (
+        quant_resolved is None
+        and not server_args._quantization_explicitly_unset
+        and quant_method is not None
+    ):
+        overrides["quantization"] = quant_method
+        quant_resolved = quant_method
+
+    if get_platform().is_hip:
+        if is_attention_backend_not_set(cfg):
+            overrides["attention_backend"] = "triton"
+        if cfg.moe_runner_backend == "auto" and quant_resolved == "mxfp8":
+            overrides["moe_runner_backend"] = "triton"
+        if not envs.USE_ROCM_AITER_ROPE_BACKEND.is_set():
+            envs.USE_ROCM_AITER_ROPE_BACKEND.set("0")
+        aiter_fusion_resolved = cfg.enable_aiter_allreduce_fusion
+        if cfg.ep_size > 1 and cfg.moe_a2a_backend == "none" and aiter_fusion_resolved:
+            logger.warning(
+                "Disable --enable-aiter-allreduce-fusion for MiniMax-M3 "
+                "standard EP on ROCm because the deferred fused all-reduce "
+                "corrupts sparse MoE partial outputs."
+            )
+            overrides["enable_aiter_allreduce_fusion"] = False
+            aiter_fusion_resolved = False
+        # By default MiniMax-M3 on ROCm keeps NCCL all-reduce (custom AR off)
+        # whenever aiter all-reduce fusion is not used. Opting in via
+        # SGLANG_M3_ALLOW_CUSTOM_AR keeps custom all-reduce enabled so the
+        # quick-reduce path (ROCM_QUICK_REDUCE_QUANTIZATION=INT4/INT6/INT8) can
+        # accelerate the large prefill all-reduce.
+        if not aiter_fusion_resolved and not envs.SGLANG_M3_ALLOW_CUSTOM_AR.get():
+            overrides["disable_custom_all_reduce"] = True
+    elif get_platform().is_sm100:
+        if is_attention_backend_not_set(cfg):
+            if (
+                cfg.kv_cache_dtype == "fp8_e4m3"
+                and not envs.SGLANG_DISABLE_M3_FP8_ATTN_GEMM.get()
+            ):
+                # fp8 attention GEMMs activate whenever possible
+                # (m3_fp8_attn_gemm_enabled); only trtllm_mha serves the dense
+                # fp8-q path, so prefer it over fa4 for fp8 KV. The
+                # SGLANG_DISABLE_M3_FP8_ATTN_GEMM kill switch keeps the fa4
+                # default (pre-fp8 behavior).
+                overrides["attention_backend"] = "trtllm_mha"
+            else:
+                overrides["attention_backend"] = "fa4"
+        backend_resolved = overrides.get("attention_backend", cfg.attention_backend)
+        page_resolved = cfg.page_size
+        # fa4 (fmha_sm100) and trtllm_mha both allow the page_size == 128
+        # sparse block MSA needs (trtllm_mha via trtllm-gen's dynamic
+        # tokens-per-page kernels).
+        if page_resolved is None and backend_resolved in ("fa4", "trtllm_mha"):
+            overrides["page_size"] = 128
+            page_resolved = 128
+        if cfg.moe_runner_backend == "auto" and quant_resolved == "mxfp8":
+            overrides["moe_runner_backend"] = "deep_gemm"
+        elif cfg.moe_runner_backend == "auto" and quant_resolved == "modelopt_mixed":
+            overrides["moe_runner_backend"] = "flashinfer_trtllm_routed"
+        logger.info(
+            "MiniMax-M3 on SM100: attention_backend="
+            f"{overrides.get('attention_backend', cfg.attention_backend)}, page_size={page_resolved}, "
+            f"moe_runner_backend={overrides.get('moe_runner_backend', cfg.moe_runner_backend)}."
+        )
+    elif get_platform().is_sm90:
+        if is_attention_backend_not_set(cfg):
+            overrides["attention_backend"] = "fa3"
+        page_resolved = cfg.page_size
+        if (
+            page_resolved is None
+            and overrides.get("attention_backend", cfg.attention_backend) == "fa3"
+        ):
+            overrides["page_size"] = 128
+            page_resolved = 128
+        logger.info(
+            "MiniMax-M3 on Hopper: attention_backend="
+            f"{overrides.get('attention_backend', cfg.attention_backend)}, page_size={page_resolved} "
+            "(MSA is SM100-only; sparse attention runs on the Triton path)."
+        )
+
+    # fp8 attention GEMMs have no opt-in flag: m3_fp8_attn_gemm_enabled
+    # (server_args.py) derives the mode from kv_cache_dtype (fp8_e4m3) +
+    # attention_backend (trtllm_mha) + SM100 at runtime. Surface the
+    # resolution here: warn on fp8_e5m2 (fmha_sm100's variant lookup would
+    # silently dispatch the e4m3 kernel, so e5m2 stays on the widening Triton
+    # path), log when the fp8 GEMM mode is active, and log when the
+    # SGLANG_DISABLE_M3_FP8_ATTN_GEMM kill switch suppresses it.
+    if cfg.kv_cache_dtype == "fp8_e5m2":
+        logger.warning(
+            "MiniMax-M3 with kv_cache_dtype fp8_e5m2: fp8 attention GEMMs stay "
+            "DISABLED (fmha_sm100's variant lookup would silently dispatch the "
+            "e4m3 kernel for e5m2); sparse attention runs on the widening "
+            "Triton path. Use --kv-cache-dtype fp8_e4m3 for fp8 attention GEMMs."
+        )
+    elif (
+        cfg.kv_cache_dtype == "fp8_e4m3"
+        and overrides.get("attention_backend", cfg.attention_backend) == "trtllm_mha"
+        and get_platform().is_sm100
+    ):
+        if envs.SGLANG_DISABLE_M3_FP8_ATTN_GEMM.get():
+            logger.info(
+                "MiniMax-M3 fp8 attention GEMMs DISABLED by "
+                "SGLANG_DISABLE_M3_FP8_ATTN_GEMM: bf16 indexer + widening "
+                "Triton sparse path, bf16 q; dense layers keep trtllm_mha's "
+                "fp8 KV cache."
+            )
+        else:
+            logger.info(
+                "MiniMax-M3 fp8 attention GEMMs active (kv_cache_dtype fp8_e4m3 + "
+                "trtllm_mha on SM100): fp8 main/index KV, fp8-cast q, fp8 "
+                "sparse/MSA kernels. Set SGLANG_DISABLE_M3_FP8_ATTN_GEMM=1 to "
+                "force the pre-fp8 numerics."
+            )
+
+    moe_runner_resolved = overrides.get("moe_runner_backend", cfg.moe_runner_backend)
+    if quant_resolved is None and moe_runner_resolved in ("auto", "deep_gemm"):
+        if moe_runner_resolved == "deep_gemm":
+            logger.warning(
+                "MiniMax-M3: the deep_gemm MoE runner produces corrupted output "
+                "on bf16 full weights; overriding --moe-runner-backend to 'triton'."
+            )
+        overrides["moe_runner_backend"] = "triton"
+
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/moss_vl.py b/python/sglang/srt/arg_groups/model_overrides/moss_vl.py
new file mode 100644
index 000000000..d0880e1b2
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/moss_vl.py
@@ -0,0 +1,34 @@
+"""Config-time override declarations for moss_vl.
+
+Architectures: MossVLForConditionalGeneration.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    attention_backends_of,
+    is_attention_backend_not_set,
+    resolved_view,
+    resolving_view,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("MossVLForConditionalGeneration")
+def _moss_vl_overrides(server_args: Any, hf_config: Any) -> dict:
+    overrides: Dict[str, Any] = {}
+    if is_attention_backend_not_set(resolving_view(server_args)):
+        overrides["prefill_attention_backend"] = "flashinfer"
+        logger.info("Use flashinfer as default prefill attention backend for Moss-VL")
+    prefill_backend = (
+        overrides.get("prefill_attention_backend")
+        or attention_backends_of(resolved_view(server_args))[0]
+    )
+    assert prefill_backend == "flashinfer", (
+        "MossVLForConditionalGeneration requires flashinfer prefill "
+        "attention backend for cross-attention custom mask support."
+    )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/muse_glimmer.py b/python/sglang/srt/arg_groups/model_overrides/muse_glimmer.py
new file mode 100644
index 000000000..7a6f32cdb
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/muse_glimmer.py
@@ -0,0 +1,24 @@
+"""Config-time override declarations for muse_glimmer.
+
+Architectures: MuseGlimmerForCausalLM, MuseGlimmerForConditionalGeneration.
+"""
+
+import logging
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("MuseGlimmerForConditionalGeneration", "MuseGlimmerForCausalLM")
+def _muse_glimmer_fp4_gemm_runner_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if get_platform().is_sm120 and cfg.fp4_gemm_runner_backend == "auto":
+        logger.info("Use marlin as FP4 GEMM runner backend on SM120 for Muse Glimmer")
+        return {"fp4_gemm_runner_backend": "marlin"}
+    return {}
diff --git a/python/sglang/srt/arg_groups/model_overrides/nemotron_h.py b/python/sglang/srt/arg_groups/model_overrides/nemotron_h.py
new file mode 100644
index 000000000..d316db88f
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/nemotron_h.py
@@ -0,0 +1,123 @@
+"""Config-time override declarations for nemotron_h.
+
+Architectures: NemotronHForCausalLM, NemotronHPuzzleForCausalLM.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    is_attention_backend_not_set,
+    model_config_of,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("NemotronHForCausalLM", "NemotronHPuzzleForCausalLM")
+def _nemotron_h_overrides(server_args: Any, hf_config: Any) -> dict:
+    """NemotronH quantization / MoE runner / attention backend defaults
+    (absorbed from the retired arg_groups/nemotron_h_hook.py; the mamba radix
+    cache handling and the triton-backend assert stay in the arch branch)."""
+    cfg = resolving_view(server_args)
+    model_arch = hf_config.architectures[0]
+    model_config = model_config_of(server_args)
+    overrides: Dict[str, Any] = {}
+
+    is_modelopt = model_config.quantization in [
+        "modelopt",
+        "modelopt_fp8",
+        "modelopt_fp4",
+        "modelopt_mixed",
+    ]
+    quantization = cfg.quantization
+    if is_modelopt:
+        assert model_config.hf_config.mlp_hidden_act == "relu2"
+        if model_config.quantization == "modelopt":
+            quant_algo = model_config.hf_config.quantization_config["quant_algo"]
+            if quant_algo == "MIXED_PRECISION":
+                quantization = "modelopt_mixed"
+            else:
+                quantization = (
+                    "modelopt_fp4" if quant_algo == "NVFP4" else "modelopt_fp8"
+                )
+        else:
+            quantization = model_config.quantization
+        overrides["quantization"] = quantization
+
+    has_w4a16_moe_layers = False
+    if is_modelopt and quantization == "modelopt_mixed":
+        has_w4a16_moe_layers = any(
+            info.get("quant_algo") == "W4A16_NVFP4" and ".experts." in name
+            for name, info in hf_config.quantization_config.get(
+                "quantized_layers", {}
+            ).items()
+        )
+
+    if has_w4a16_moe_layers:
+        if cfg.moe_a2a_backend != "none":
+            raise ValueError("W4A16_NVFP4 MoE layers require --moe-a2a-backend=none.")
+        if cfg.moe_runner_backend not in ("auto", "marlin"):
+            raise ValueError(
+                "W4A16_NVFP4 MoE layers require --moe-runner-backend=marlin."
+            )
+        if cfg.moe_runner_backend == "auto":
+            overrides["moe_runner_backend"] = "marlin"
+            logger.info(
+                "Use marlin as MoE runner backend for "
+                f"{model_arch} with W4A16_NVFP4 MoE layers"
+            )
+    elif (is_modelopt or model_config.quantization is None) and (
+        cfg.moe_runner_backend == "auto"
+    ):
+        if get_platform().is_sm100 and cfg.moe_a2a_backend == "none":
+            overrides["moe_runner_backend"] = "flashinfer_trtllm"
+            logger.info(
+                f"Use flashinfer_trtllm as MoE runner backend on sm100 for {model_arch}"
+            )
+        elif (
+            (
+                model_config.quantization in ("modelopt_fp4", "modelopt_mixed")
+                or quantization == "modelopt_fp4"
+            )
+            and get_platform().is_cuda
+            and (8, 0) <= get_platform().device_capability < (10, 0)
+        ):
+            overrides["moe_runner_backend"] = "marlin"
+            logger.info(
+                "Use marlin as MoE runner backend on SM80-SM90 for "
+                f"{model_arch} {model_config.quantization}"
+            )
+        else:
+            overrides["moe_runner_backend"] = "flashinfer_cutlass"
+
+    if get_platform().is_blackwell and is_attention_backend_not_set(cfg):
+        if cfg.speculative_algorithm is not None:
+            speculative_algorithm = cfg.speculative_algorithm.upper()
+            if get_platform().is_sm100 and cfg.speculative_eagle_topk in (
+                None,
+                1,
+            ):
+                overrides["attention_backend"] = "trtllm_mha"
+                if cfg.page_size is None:
+                    overrides["page_size"] = 64
+                if cfg.mamba_radix_cache_strategy == "auto":
+                    overrides["mamba_radix_cache_strategy"] = "extra_buffer"
+                if (
+                    cfg.speculative_draft_attention_backend is None
+                    and speculative_algorithm in ("EAGLE", "NEXTN", "DSPARK")
+                ):
+                    overrides["speculative_draft_attention_backend"] = "trtllm_mha"
+            else:
+                overrides["attention_backend"] = "triton"
+                if (
+                    cfg.speculative_draft_attention_backend is None
+                    and speculative_algorithm in ("EAGLE", "NEXTN", "DFLASH", "DSPARK")
+                ):
+                    overrides["speculative_draft_attention_backend"] = "flashinfer"
+        elif get_platform().is_sm100:
+            overrides["attention_backend"] = "trtllm_mha"
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/olmo2.py b/python/sglang/srt/arg_groups/model_overrides/olmo2.py
new file mode 100644
index 000000000..9dc819ca2
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/olmo2.py
@@ -0,0 +1,34 @@
+"""Config-time override declarations for olmo2.
+
+Architectures: Olmo2ForCausalLM.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("Olmo2ForCausalLM")
+def _olmo2_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+    # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with Olmo3 model.
+    logger.warning(
+        f"Disabling hybrid SWA memory for {hf_config.architectures[0]} as it is not yet supported."
+    )
+    overrides["disable_hybrid_swa_memory"] = True
+    if cfg.attention_backend is None:
+        if get_platform().is_cuda and get_platform().is_sm100:
+            overrides["attention_backend"] = "trtllm_mha"
+        elif get_platform().is_cuda and get_platform().device_sm >= 80:
+            overrides["attention_backend"] = "fa3"
+        else:
+            overrides["attention_backend"] = "triton"
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/qwen3_5.py b/python/sglang/srt/arg_groups/model_overrides/qwen3_5.py
new file mode 100644
index 000000000..77976bbee
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/qwen3_5.py
@@ -0,0 +1,54 @@
+"""Config-time override declarations for qwen3_5.
+
+Architectures: InternS2MobiusForConditionalGeneration, InternS2PreviewForConditionalGeneration, Qwen3NextForCausalLM, Qwen3_5ForConditionalGeneration, Qwen3_5MoeForConditionalGeneration.
+"""
+
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    get_default_attn_backend,
+    mamba_extra_buffer_of,
+    model_config_of,
+    resolved_view,
+    resolving_view,
+    use_mla_backend,
+)
+from sglang.srt.runtime_context import get_platform
+
+
+@_register_for(
+    "Qwen3NextForCausalLM",
+    "Qwen3_5MoeForConditionalGeneration",
+    "InternS2PreviewForConditionalGeneration",
+    "InternS2MobiusForConditionalGeneration",
+    "Qwen3_5ForConditionalGeneration",
+)
+def _qwen3_5_hybrid_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    if not get_platform().is_sm100 or cfg.attention_backend is not None:
+        return {}
+    sm100_default_attn_backend = "triton"
+    # trtllm_mha requires speculative_eagle_topk == 1 and page_size > 1.
+    # get_default_attn_backend handles the eagle_topk check.
+    # There is only one case where page_size=1 is required,
+    # which is when radix cache is enabled and both extra_buffer
+    # and spec decoding are disabled.
+    default_attn_backend = get_default_attn_backend(
+        server_args,
+        use_mla_backend=use_mla_backend(server_args),
+        model_config=model_config_of(server_args),
+    )
+    # The mamba radix-cache pass runs before this dispatch: read the
+    # declared strategy through the view (the legacy branch observed the
+    # already-written field here).
+    if default_attn_backend == "trtllm_mha" and not (
+        not mamba_extra_buffer_of(resolved_view(server_args))
+        and not cfg.disable_radix_cache
+        and cfg.speculative_algorithm is None
+    ):
+        sm100_default_attn_backend = "trtllm_mha"
+    return {
+        "attention_backend": sm100_default_attn_backend,
+        "page_size": 64 if sm100_default_attn_backend == "trtllm_mha" else 1,
+    }
diff --git a/python/sglang/srt/arg_groups/model_overrides/qwen3_moe.py b/python/sglang/srt/arg_groups/model_overrides/qwen3_moe.py
new file mode 100644
index 000000000..2eeb402bd
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/qwen3_moe.py
@@ -0,0 +1,50 @@
+"""Config-time override declarations for qwen3_moe.
+
+Architectures: InternS2PreviewForConditionalGeneration, Qwen3MoeForCausalLM, Qwen3NextForCausalLM, Qwen3VLMoeForConditionalGeneration, Qwen3_5ForConditionalGeneration, Qwen3_5MoeForConditionalGeneration.
+"""
+
+import logging
+from typing import Any, Dict
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.runtime_context import get_platform
+from sglang.srt.utils.common import get_quantization_config
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for(
+    "Qwen3MoeForCausalLM",
+    "Qwen3VLMoeForConditionalGeneration",
+    "Qwen3NextForCausalLM",
+    "Qwen3_5MoeForConditionalGeneration",
+    "InternS2PreviewForConditionalGeneration",
+    "Qwen3_5ForConditionalGeneration",
+)
+def _qwen3_moe_family_overrides(server_args: Any, hf_config: Any) -> dict:
+    cfg = resolving_view(server_args)
+    overrides: Dict[str, Any] = {}
+    if get_platform().is_sm100:
+        quant_method = get_quantization_config(hf_config)
+        quantization = cfg.quantization
+        if (
+            quantization is None
+            and not server_args._quantization_explicitly_unset
+            and quant_method is not None
+        ):
+            overrides["quantization"] = quant_method
+            quantization = quant_method
+        if (
+            (quantization in ("fp8", "modelopt_fp4") or quantization is None)
+            and cfg.moe_a2a_backend == "none"
+            and cfg.moe_runner_backend == "auto"
+        ):
+            overrides["moe_runner_backend"] = "flashinfer_trtllm"
+            logger.info(
+                "Use flashinfer_trtllm as MoE runner backend on sm100 for "
+                f"{hf_config.architectures[0]}"
+            )
+    return overrides
diff --git a/python/sglang/srt/arg_groups/model_overrides/qwen3_vl.py b/python/sglang/srt/arg_groups/model_overrides/qwen3_vl.py
new file mode 100644
index 000000000..0dcbede98
--- /dev/null
+++ b/python/sglang/srt/arg_groups/model_overrides/qwen3_vl.py
@@ -0,0 +1,32 @@
+"""Config-time override declarations for qwen3_vl.
+
+Architectures: Qwen3VLForConditionalGeneration.
+"""
+
+import logging
+from typing import Any
+
+from sglang.srt.arg_groups.model_override_base import (
+    _register_for,
+    resolving_view,
+)
+from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_platform
+
+logger = logging.getLogger(__name__)
+
+
+@_register_for("Qwen3VLForConditionalGeneration")
+def _qwen3vl_overrides(server_args: Any, hf_config: Any) -> dict:
+
+    cfg = resolving_view(server_args)
+    if (
+        get_platform().is_hip
+        and envs.SGLANG_USE_AITER_UNIFIED_ATTN.get()
+        and cfg.page_size is None
+    ):
+        logger.info(
+            "Setting page_size=16 for aiter unified attention on Qwen3VLForConditionalGeneration."
+        )
+        return {"page_size": 16}
+    return {}
diff --git a/python/sglang/srt/arg_groups/overrides.py b/python/sglang/srt/arg_groups/overrides.py
index 36a47cd60..27703d3b0 100644
--- a/python/sglang/srt/arg_groups/overrides.py
+++ b/python/sglang/srt/arg_groups/overrides.py
@@ -35,155 +35,51 @@ from __future__ import annotations
 
 import copy
 import dataclasses
-import inspect
 import json
 import logging
 import math
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
 
+from sglang.srt.arg_groups import model_override_base
 from sglang.srt.arg_groups.arg_utils import field_names, resolvable_fields
+
+# Re-exported for the callers that already import these names from here; the
+# declarations under ``model_overrides/`` import them from the base directly.
+from sglang.srt.arg_groups.model_override_base import (  # noqa: F401
+    _MODEL_OVERRIDE_FNS,
+    _PREDICATE_OVERRIDE_FNS,
+    MODEL_OVERRIDES,
+    ResolvedView,
+    ResolvingConfig,
+    _declaration_overlay,
+    _invoke_provider,
+    _register_for,
+    attention_backends_of,
+    get_default_attn_backend,
+    is_attention_backend_not_set,
+    mamba_extra_buffer_of,
+    model_config_of,
+    record_of,
+    register_model_override,
+    register_model_override_predicate,
+    resolved_view,
+    resolving_view,
+    use_mla_backend,
+)
+
+logger = logging.getLogger(__name__)
 from sglang.srt.environ import envs
-from sglang.srt.hardware_backend.mlx.runtime import use_mlx
 from sglang.srt.model_executor.cuda_graph_config import Backend
-from sglang.srt.platforms import current_platform
 from sglang.srt.runtime_context import (
     get_context,
     get_platform,
 )
 from sglang.srt.utils.common import (
-    get_device_name,
-    get_nvidia_driver_version,
     get_quantization_config,
-    is_cpu,
     is_gfx95_supported,
-    is_mnnvl_fabric_device,
-    is_mps,
-    is_no_spec_infer_or_topk_one,
-    is_triton_kernels_available,
     xpu_has_xmx_support,
 )
 
-logger = logging.getLogger(__name__)
-
-# Constant per-architecture overrides (populated by the migration sweeps).
-MODEL_OVERRIDES: Dict[str, Dict[str, Any]] = {
-    # These models run in bfloat16 regardless of the requested dtype
-    # (faithful port of the legacy unconditional arch branch).
-    "MistralLarge3ForCausalLM": {"dtype": "bfloat16"},
-    "PixtralForConditionalGeneration": {"dtype": "bfloat16"},
-}
-
-# Derived per-architecture override providers, in registration order.
-_MODEL_OVERRIDE_FNS: Dict[str, List[Callable[..., dict]]] = {}
-
-# Predicate-keyed providers, in registration order — for legacy branches
-# matched by substring/predicate on the architecture string rather than an
-# exact name (e.g. '"Step3p5ForCausalLM" in model_arch').
-_PREDICATE_OVERRIDE_FNS: List[Tuple[Callable[[str], bool], Callable[..., dict]]] = []
-
-
-def register_model_override(architecture: str):
-    """Register a derived-override provider for ``architecture``.
-
-    The decorated callable receives ``(server_args, hf_config)``, must not
-    mutate either, and returns a ``{field: resolved_value}`` dict (possibly
-    empty when nothing applies). Providers needing derived model data beyond
-    the HF config go through ``model_config_of(server_args)`` (cached,
-    read-only) — never anything mutating.
-    """
-
-    def decorator(fn: Callable[..., dict]) -> Callable[..., dict]:
-        _MODEL_OVERRIDE_FNS.setdefault(architecture, []).append(fn)
-        return fn
-
-    return decorator
-
-
-def register_model_override_predicate(predicate: Callable[[str], bool]):
-    """Register a derived-override provider keyed by an architecture
-    predicate. Same callable contract as ``register_model_override``."""
-
-    def decorator(fn: Callable[..., dict]) -> Callable[..., dict]:
-        _PREDICATE_OVERRIDE_FNS.append((predicate, fn))
-        return fn
-
-    return decorator
-
-
-def _invoke_provider(
-    fn: Callable[..., dict], server_args: Any, hf_config: Any
-) -> Dict[str, Any]:
-    declared = fn(server_args, hf_config)
-    if not isinstance(declared, dict):
-        raise TypeError(
-            f"model override provider {fn.__qualname__} must return a dict, "
-            f"got {type(declared).__name__}"
-        )
-    return declared
-
-
-class ResolvedView:
-    """Read-only view of the resolving configuration handed to post-process
-    passes: the accumulated declarations overlaid on the pristine
-    ``server_args`` (residual imperative writes of non-resolved fields show
-    through the fallthrough) — exactly the state the legacy handler at the
-    same slot observed. Writes are rejected: passes return declarations.
-    """
-
-    __slots__ = ("_server_args", "_overlay")
-
-    def __init__(self, server_args: Any, overlay: Optional[Dict[str, Any]] = None):
-        object.__setattr__(self, "_server_args", server_args)
-        object.__setattr__(self, "_overlay", overlay or {})
-
-    def __getattr__(self, name: str) -> Any:
-        overlay = object.__getattribute__(self, "_overlay")
-        if name in overlay:
-            return overlay[name]
-        return getattr(object.__getattribute__(self, "_server_args"), name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        raise AttributeError(
-            "ResolvedView is read-only; post-process passes return declarations"
-        )
-
-
-class ResolvingConfig:
-    """Live read view of the resolution result: the declaration stash over the
-    record's fields, looked up per read.
-
-    ``ResolvedView`` snapshots the overlay when it is built, which is what a
-    post-process pass wants -- it reads the state at its slot. A resolver that
-    reads *after* declaring, or after calling something that declares, needs the
-    current answer instead, so this one walks the stash on every read. It falls
-    through to the field, which is where the raw input lives.
-    """
-
-    __slots__ = ("_server_args",)
-
-    def __init__(self, server_args: Any):
-        object.__setattr__(self, "_server_args", server_args)
-
-    def __getattr__(self, name: str) -> Any:
-        server_args = object.__getattribute__(self, "_server_args")
-        for _source, declared in reversed(
-            getattr(server_args, "_resolved_overrides", None) or ()
-        ):
-            if name in declared:
-                return declared[name]
-        return getattr(server_args, name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        raise AttributeError(
-            "ResolvingConfig is read-only; resolution writes through declarations"
-        )
-
-
-def resolving_view(server_args: Any) -> ResolvingConfig:
-    """A live read view of what resolution has decided so far."""
-    return ResolvingConfig(server_args)
-
-
 # Registered post-process passes. This is a registry, not an execution order:
 # each pass is invoked from its own slot via run_post_process_pass.
 POST_PROCESS_PASSES: List[Callable[..., dict]] = []
@@ -199,17 +95,6 @@ def register_post_process(fn: Callable[..., dict]) -> Callable[..., dict]:
     return fn
 
 
-def _declaration_overlay(server_args: Any) -> Dict[str, Any]:
-    """What the declarations say so far, last writer wins.
-
-    Nothing writes the fields, so a mid-resolution reader needs this to see a
-    decision at all; the fields keep what the caller supplied."""
-    overlay: Dict[str, Any] = {}
-    for _source, declared in getattr(server_args, "_resolved_overrides", None) or ():
-        overlay.update(declared)
-    return overlay
-
-
 def run_post_process_pass(server_args: Any, fn: Callable[..., dict]) -> None:
     """Invoke one pass at its legacy handler slot.
 
@@ -436,50 +321,66 @@ def _plain(value: Any) -> Any:
     return copy.deepcopy(value)
 
 
-def resolved_view(server_args: Any) -> ResolvedView:
-    """Read-only view of the resolving configuration: the declarations
-    overlaid on the fields, snapshotted per call.
+def pre_capture_activation_reserve_mb_of(cfg: Any, gpu_mem: Optional[float]) -> float:
+    """The activation working-set reserve held back before cuda-graph capture.
 
-    For mid-resolution code that is not a pass (``__post_init__`` handlers and
-    hooks) that must answer with what resolution decided -- a declaration-only resolver (a model-specific
-    override, a registry entry) never writes the field, so a field read there
-    answers with the raw input."""
-    return ResolvedView(server_args, overlay=_declaration_overlay(server_args))
+    The config-shaped half of the pair; `runtime_context` carries the
+    published-bag half, and `TestDerivedPredicatesAgreeAcrossTiers` pins the
+    two equal.
+    """
+    if cfg.disaggregation_mode == "decode":
+        running_requests = (
+            cfg.max_running_requests or cfg.cuda_graph_config.decode.max_bs or 1
+        )
+        activation_tokens = max(
+            running_requests * (cfg.speculative_num_draft_tokens or 1), 2048
+        )
+    elif cfg.chunked_prefill_size > 0:
+        activation_tokens = max(cfg.chunked_prefill_size, 2048)
+    else:
+        activation_tokens = max(cfg.max_prefill_tokens, 2048)
+    reserved_mem = 512 + activation_tokens * 1.5 + cfg.tp_size * cfg.pp_size / 8 * 1024
+    if gpu_mem is not None and gpu_mem > 60 * 1024:
+        reserved_mem = max(reserved_mem, 10 * 1024)
+    return reserved_mem
 
 
-def attention_backends_of(cfg: Any) -> tuple:
-    """(prefill, decode) attention backends of a config-shaped object (a
-    ResolvedView mid-resolution, or pristine server_args at dispatch time):
-    split fields fall back to the base backend."""
-    prefill = (
-        cfg.prefill_attention_backend
-        if cfg.prefill_attention_backend
-        else cfg.attention_backend
-    )
-    decode = (
-        cfg.decode_attention_backend
-        if cfg.decode_attention_backend
-        else cfg.attention_backend
-    )
-    return prefill, decode
+def kv_event_block_size_of(cfg: Any) -> int:
+    """Width KV events are emitted at.
+
+    Under DCP the radix tree pages at ``page_size * dcp_size``
+    (`mem_cache/kv_cache_builder.py`), and subscribers key on this.
+    """
+    return cfg.page_size * cfg.dcp_size
+
+
+def modelexpress_config_of(cfg: Any) -> dict:
+    """``modelexpress_config`` parsed.
+
+    It is a JSON string (or an already-parsed dict) rather than a leaf of its
+    own, so everything that wants a key out of it goes through here -- one parse,
+    for every reader.
+    """
+    raw = cfg.modelexpress_config
+    if raw is None:
+        return {}
+    if isinstance(raw, str):
+        return json.loads(raw)
+    return raw
+
+
+def modelexpress_url_of(cfg: Any) -> Optional[str]:
+    """The modelexpress endpoint a config-shaped object points at."""
+    return modelexpress_config_of(cfg).get("url")
 
 
 def modelexpress_transport_of(cfg: Any) -> str:
     """The modelexpress transport a config-shaped object asks for.
 
-    ``modelexpress_config`` is a JSON string (or an already-parsed dict) rather
-    than a leaf of its own; this is the shared parse for the transfer-engine
-    gate (`remote_instance_transfer_engine_of`) and any future bag reader.
-    ``ServerArgs.modelexpress_transport`` keeps its own instance-cached parse
-    (`_parsed_modelexpress_config`) -- same rule, cached seed-side."""
-    raw = cfg.modelexpress_config
-    if raw is None:
-        parsed = {}
-    elif isinstance(raw, str):
-        parsed = json.loads(raw)
-    else:
-        parsed = raw
-    return parsed.get("transport", "nixl")
+    The shared parse for the transfer-engine gate
+    (`remote_instance_transfer_engine_of`) and any bag reader.
+    """
+    return modelexpress_config_of(cfg).get("transport", "nixl")
 
 
 def remote_instance_transfer_engine_of(cfg: Any, load_format: Any = None) -> bool:
@@ -500,19 +401,6 @@ def remote_instance_transfer_engine_of(cfg: Any, load_format: Any = None) -> boo
     )
 
 
-def mamba_extra_buffer_of(cfg: Any) -> bool:
-    """Mid-resolution equivalent of runtime_context.mamba_extra_buffer_enabled:
-    reads the (possibly overlaid) strategy from a config-shaped object.
-
-    This is the one definition of the predicate: ``ServerArgs`` delegates its
-    member to it, and the runtime_context accessor is its post-publish sibling
-    (which cannot reuse it, because the two leaves land in different bags)."""
-    return cfg.disable_radix_cache is False and cfg.mamba_radix_cache_strategy in (
-        "extra_buffer",
-        "extra_buffer_lazy",
-    )
-
-
 def mamba_extra_buffer_lazy_of(cfg: Any) -> bool:
     """The lazy variant of :func:`mamba_extra_buffer_of`."""
     return (
@@ -531,15 +419,17 @@ def collect_model_override_declarations(
     registration order, then matching predicate-keyed callables in
     registration order. Empty declarations are dropped.
     """
+    # Off the module, not through the imported names: the registrars append to
+    # the base's objects, and a copied name here would be a second binding.
     declarations: List[Tuple[str, Dict[str, Any]]] = []
-    const = MODEL_OVERRIDES.get(architecture)
+    const = model_override_base.MODEL_OVERRIDES.get(architecture)
     if const:
         declarations.append((f"MODEL_OVERRIDES[{architecture!r}]", dict(const)))
-    for fn in _MODEL_OVERRIDE_FNS.get(architecture, ()):
+    for fn in model_override_base._MODEL_OVERRIDE_FNS.get(architecture, ()):
         declared = _invoke_provider(fn, server_args, hf_config)
         if declared:
             declarations.append((fn.__qualname__, dict(declared)))
-    for predicate, fn in _PREDICATE_OVERRIDE_FNS:
+    for predicate, fn in model_override_base._PREDICATE_OVERRIDE_FNS:
         if predicate(architecture):
             declared = _invoke_provider(fn, server_args, hf_config)
             if declared:
@@ -554,1180 +444,8 @@ def collect_model_override_declarations(
 # ---------------------------------------------------------------------------
 
 
-def _register_for(*architectures: str):
-    """Register one provider for several architectures (family lists)."""
-
-    def decorator(fn: Callable[..., dict]) -> Callable[..., dict]:
-        for architecture in architectures:
-            register_model_override(architecture)(fn)
-        return fn
-
-    return decorator
-
-
-def _dspark_verify_on_decode_backend(
-    backend: Optional[str], q_len: int, kv_cache_dtype: Optional[str]
-) -> bool:
-    """Whether the MLA decode backend can serve a q_len-wide target verify."""
-    if backend == "trtllm_mla":
-        return True
-    if backend == "tokenspeed_mla":
-        return kv_cache_dtype == "fp8_e4m3" and q_len <= 8
-    if backend == "cutedsl_mla":
-        # cute-dsl monolithic MLA decode folds the verify tokens into the head
-        # dim (fold_sq), so it serves any DSPARK verify width. Needs flashinfer
-        # >= 0.6.15 (older builds reject q_len >= 5).
-        return True
-    return False
-
-
-def _require_kimi_k3_cutedsl_dcp_support() -> None:
-    try:
-        from flashinfer.decode import trtllm_batch_decode_with_kv_cache_mla
-
-        parameters = inspect.signature(trtllm_batch_decode_with_kv_cache_mla).parameters
-    except (ImportError, TypeError, ValueError) as exc:
-        raise RuntimeError(
-            "Kimi-K3 DCP with decode_attention_backend='cutedsl_mla' requires "
-            "FlashInfer 0.6.17 or newer with "
-            "trtllm_batch_decode_with_kv_cache_mla exposing enable_dcp."
-        ) from exc
-
-    if "enable_dcp" not in parameters:
-        raise RuntimeError(
-            "Kimi-K3 DCP with decode_attention_backend='cutedsl_mla' requires "
-            "enable_dcp in the signature of "
-            "flashinfer.decode.trtllm_batch_decode_with_kv_cache_mla; upgrade "
-            "to FlashInfer 0.6.17 or newer."
-        )
-
-
-@_register_for("KimiK3ForConditionalGeneration")
-def _kimi_k3_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if cfg.dcp_size > 1:
-        overrides = {}
-        if cfg.enable_symm_mem:
-            logger.warning(
-                "Kimi-K3 DCP disables --enable-symm-mem due to decode CUDA "
-                "graph correctness issues."
-            )
-            overrides["enable_symm_mem"] = False
-
-        if cfg.speculative_algorithm == "DSPARK":
-            from sglang.srt.speculative.ragged_verify import (
-                RaggedVerifyMode,
-                read_ragged_verify_mode,
-            )
-
-            ragged_mode = read_ragged_verify_mode()
-            if ragged_mode is not RaggedVerifyMode.STATIC:
-                raise ValueError(
-                    "Kimi-K3 DCP + DSPARK currently requires "
-                    "SGLANG_RAGGED_VERIFY_MODE=static; compact/cap-accept are "
-                    f"not validated under DCP (got {ragged_mode.value!r})."
-                )
-
-            # DSPARK target-verify + draft-extend must run on the decode
-            # (cutedsl_mla) backend, whose _run_decode_kernel implements the DCP
-            # signature (causal_seqs / cp_world / cp_rank). The default
-            # "prefill" routes verify to trtllm_mla, whose base _run_decode_kernel
-            # lacks that DCP path (TypeError: unexpected kwarg 'causal_seqs').
-            overrides["speculative_attention_mode"] = "decode"
-
-        prefill_backend, decode_backend = attention_backends_of(cfg)
-        if decode_backend == "cutedsl_mla" or decode_backend is None:
-            _require_kimi_k3_cutedsl_dcp_support()
-            logger.info(
-                "Kimi-K3 DCP keeps decode attention backend 'cutedsl_mla' "
-                f"(prefill={prefill_backend!r} -> 'trtllm_mla')."
-            )
-            overrides.update(
-                prefill_attention_backend="trtllm_mla",
-                decode_attention_backend="cutedsl_mla",
-            )
-        elif decode_backend == "tokenspeed_mla":
-            logger.info(
-                "Kimi-K3 DCP overrides attention backends: "
-                f"prefill={prefill_backend!r}, decode={decode_backend!r} -> "
-                "'tokenspeed_mla'."
-            )
-            logger.info(
-                "Kimi-K3 DCP with tokenspeed mla backend overrides KV cache dtype: "
-                f"{cfg.kv_cache_dtype!r} -> 'fp8_e4m3'."
-            )
-            overrides.update(
-                prefill_attention_backend="tokenspeed_mla",
-                decode_attention_backend="tokenspeed_mla",
-                kv_cache_dtype="fp8_e4m3",
-            )
-        else:
-            raise AssertionError(
-                f"Decode attention backend for Kimi-K3 DCP must be 'cutedsl_mla' or 'tokenspeed_mla', got {decode_backend!r}."
-            )
-
-        if cfg.dcp_replicate_q_proj is None:
-            logger.info("Kimi-K3 DCP enables replicated Q projection by default.")
-            overrides["dcp_replicate_q_proj"] = True
-
-        device_name = get_device_name()
-        dcp_comm_backend = "fi_a2a" if is_mnnvl_fabric_device() else "a2a"
-        logger.info(
-            "Kimi-K3 DCP selects communication backend on "
-            f"{device_name!r}: {cfg.dcp_comm_backend!r} -> "
-            f"{dcp_comm_backend!r}."
-        )
-        overrides["dcp_comm_backend"] = dcp_comm_backend
-        return overrides
-
-    if not (get_platform().is_sm100 and get_platform().device_sm in (100, 103)):
-        return {}
-    backends_unset = is_attention_backend_not_set(cfg)
-    if cfg.speculative_algorithm != "DSPARK":
-        if not backends_unset:
-            return {}
-        logger.info(
-            "Use trtllm_mla as the default prefill and decode attention "
-            "backend for Kimi-K3 on SM100/SM103."
-        )
-        return {
-            "decode_attention_backend": "trtllm_mla",
-            "prefill_attention_backend": "trtllm_mla",
-        }
-    # DSPARK: verify runs on the decode backend (mode=decode below), so this
-    # picks the verify kernel -- mode=prefill routes it to flashinfer, which is
-    # slow and syncs, while plain decode is cold under dspark.
-    q_len = cfg.speculative_num_draft_tokens or (
-        cfg.speculative_dspark_block_size + 1
-        if cfg.speculative_dspark_block_size is not None
-        # Checkpoint auto-infer happens after overrides; K3 draft uses block 7.
-        else 8
-    )
-    overrides = {}
-    if backends_unset:
-        backend = "trtllm_mla"
-        overrides["decode_attention_backend"] = backend
-        overrides["prefill_attention_backend"] = "trtllm_mla"
-    else:
-        # Explicit backend knobs keep priority, but the mode is a separate knob
-        # that still needs declaring -- else verify stays on the prefill backend,
-        # whose host-side plan (flashinfer by default) forces a per-step D2H.
-        _, backend = attention_backends_of(cfg)
-    if _dspark_verify_on_decode_backend(backend, q_len, cfg.kv_cache_dtype):
-        overrides["speculative_attention_mode"] = "decode"
-        logger.info(
-            "Kimi-K3 DSPARK on SM100/SM103: decode/verify attention backend "
-            f"{backend} (speculative_attention_mode=decode)."
-        )
-    else:
-        logger.warning(
-            f"Kimi-K3 DSPARK: decode attention backend {backend!r} cannot serve "
-            f"target verify at q_len={q_len}, so verify runs on the prefill "
-            "backend (speculative_attention_mode=prefill). A host-plan prefill "
-            "backend costs a per-step seq_lens D2H sync; leave the attention "
-            "backend knobs unset for the sync-free default."
-        )
-    return overrides
-
-
-def _is_mxfp4_pack_quantized(hf_config: Any) -> bool:
-    qc = getattr(
-        getattr(hf_config, "text_config", hf_config), "quantization_config", None
-    )
-    if not isinstance(qc, dict):
-        return False
-    groups = qc.get("config_groups") or {}
-    return any(
-        "mxfp4" in str(g.get("format", ""))
-        for g in groups.values()
-        if isinstance(g, dict)
-    )
-
-
-@_register_for("KimiK3ForConditionalGeneration")
-def _kimi_k3_moe_runner_overrides(server_args: Any, hf_config: Any) -> dict:
-    # MoE runner default, independent of the attention-backend gate above.
-    # trtllm-gen fused MoE (flashinfer_mxfp4) beats marlin on both the decode
-    # (M=bs) and the target-verify (M=bs*(gamma+1)) regimes on SM100/SM103.
-    # SM107 uses the same packed-MXFP4 runner; leaving auto unresolved falls
-    # back to BF16 weight materialization during model loading.
-    cfg = resolving_view(server_args)
-    if cfg.moe_runner_backend != "auto":
-        return {}
-    if not (get_platform().is_sm100 and get_platform().device_sm in (100, 103, 107)):
-        return {}
-    if not _is_mxfp4_pack_quantized(hf_config):
-        return {}
-    logger.info(
-        "Kimi-K3 on SM100/SM103/SM107: moe_runner_backend=flashinfer_mxfp4 "
-        "(FlashInfer SiTU kernels)."
-    )
-    return {"moe_runner_backend": "flashinfer_mxfp4"}
-
-
-@_register_for(
-    "DeepseekV3ForCausalLM",
-    "DeepseekV32ForCausalLM",
-    "KimiK25ForConditionalGeneration",
-    "MistralLarge3ForCausalLM",
-    "PixtralForConditionalGeneration",
-    "GlmMoeDsaForCausalLM",
-    "LongcatFlashForCausalLM",
-    "LongcatFlashForCausalLMNextN",
-    "Dots3NoteForCausalLM",
-)
-def _deepseek_family_overrides(server_args: Any, hf_config: Any) -> dict:
-    """Order-safe declarations of the DeepSeek/DSA branch. The CP parallel
-    writes (enable_dp_attention/ep_size/moe_a2a_backend have post-monolith
-    writers), the kv-cache/split-backend defaults, the quant/moe block (read
-    before it by _set_default_dsa_kv_cache_dtype) and the env writes stay in
-    the branch."""
-    cfg = resolving_view(server_args)
-    from sglang.srt.configs.model_config import is_deepseek_dsa
-
-    overrides: Dict[str, Any] = {}
-
-    if is_deepseek_dsa(hf_config):  # DeepSeek 3.2/GLM 5
-        # Set attention backend for DeepSeek
-        if is_attention_backend_not_set(cfg):
-            overrides["attention_backend"] = "dsa"
-            logger.info("Use dsa attention backend for DeepSeek with DSA.")
-        if not get_platform().is_npu and not get_platform().is_xpu:  # CUDA or ROCm GPU
-            if cfg.enable_prefill_cp:
-                logger.warning(
-                    "Context parallel feature is still under experiment. It has only been verified on Hopper platform."
-                )
-                overrides["enable_dp_attention"] = True
-                overrides["moe_dense_tp_size"] = 1
-                if cfg.cp_strategy == "zigzag":
-                    overrides["moe_a2a_backend"] = "deepep"
-                    overrides["ep_size"] = cfg.tp_size
-                    logger.warning(
-                        "zigzag DSA CP requires moe_dense_tp_size=1, "
-                        "moe_a2a_backend=deepep, ep_size=tp_size, batch_size=1."
-                    )
-                else:
-                    assert (
-                        cfg.dp_size == 1
-                    ), "interleave DSA CP does not support DP attention."
-                assert (
-                    cfg.tp_size <= 8
-                ), "Context parallel only supports single machine (tp_size <= 8). Cross-machine CP has precision issues."
-                # Note(kpham-sgl): Keep attn_tp_size == 1 under DSA CP.
-                # DSACPLayerCommunicator does not all-reduce attention-TP
-                # partial o_proj outputs before replicated dense FFNs.
-                attn_cp_size = cfg.tp_size // cfg.dp_size
-                overrides["attn_cp_size"] = attn_cp_size
-                logger.warning(
-                    "Enabled DSA context parallel: "
-                    f"strategy={cfg.cp_strategy}, dp_size={cfg.dp_size}, "
-                    f"moe_dense_tp_size={overrides['moe_dense_tp_size']}, "
-                    f"ep_size={overrides.get('ep_size', cfg.ep_size)}, tp_size={cfg.tp_size}, "
-                    f"attn_cp_size={attn_cp_size}, "
-                    f"kv_cache_dtype={cfg.kv_cache_dtype}, "
-                    f"moe_a2a_backend={overrides.get('moe_a2a_backend', cfg.moe_a2a_backend)}, "
-                    f"cuda_graph_config[prefill].backend=disabled"
-                )
-
-            # Deferred import to avoid a circular import at module-load
-            # time (dsa.utils imports the runtime-context accessors).
-            from sglang.srt.layers.attention.dsa.utils import (
-                aiter_can_use_preshuffle_paged_mqa,
-            )
-
-            if get_platform().is_hip and not aiter_can_use_preshuffle_paged_mqa():
-                # Legacy ROCm DSA path: aiter's gluon paged-MQA kernel is
-                # unavailable (Triton<3.5 and AITER_ENABLE_AOT_GLUON_PA_MQA_LOGITS
-                # not set, or SGLANG_DSA_HIP_DISABLE_PRESHUFFLE=1 / SGLANG_USE_AITER=0).
-                overrides["page_size"] = 1
-                logger.warning(
-                    "Setting page size to 1 for DeepSeek DSA on ROCm "
-                    "(aiter preshuffle paged-MQA path unavailable: "
-                    "needs Triton>=3.5.0 or AITER_ENABLE_AOT_GLUON_PA_MQA_LOGITS=1)."
-                )
-            else:
-                overrides["page_size"] = 64
-                logger.warning("Setting page size to 64 for DeepSeek DSA.")
-    else:
-        # DeepSeek V3/R1/V3.1
-        if get_platform().is_sm100:
-            if (
-                cfg.attention_backend is None
-                and cfg.prefill_attention_backend is None
-                and cfg.decode_attention_backend is None
-            ):
-                overrides["attention_backend"] = "trtllm_mla"
-                logger.info(
-                    "Use trtllm_mla as attention backend on sm100 for DeepseekV3ForCausalLM"
-                )
-        # MLA prefill CP auto-config. Mirrors the NSA CP block above
-        # (minus the in-seq/round-robin mode split, which MLA CP does not support)
-        if cfg.enable_prefill_cp and use_mla_backend(server_args):
-            logger.warning(
-                "MLA prefill context parallel is still experimental. "
-                "Verified on Hopper with the fa3 backend."
-            )
-            overrides["enable_dp_attention"] = True
-            # TODO(kpham-sgl) Supports moe_dense_tp_size != 1.
-            overrides["moe_dense_tp_size"] = 1
-            overrides["moe_a2a_backend"] = "deepep"
-            overrides["ep_size"] = cfg.tp_size
-            logger.warning(
-                "For MLA CP, we have the following restrictions: moe_dense_tp_size == 1, moe_a2a_backend == deepep, ep_size == tp_size, batch_size == 1"
-            )
-            # FIXME(kpham-sgl): Keep attn_tp_size == 1 under MLA CP.
-            # DSACPLayerCommunicator does not all-reduce attention-TP
-            # partial o_proj outputs before replicated dense FFNs.
-            attn_cp_size = cfg.tp_size // cfg.dp_size
-            overrides["attn_cp_size"] = attn_cp_size
-            logger.warning(
-                f"Enable Context Parallel opt for MLA, "
-                f"Setting dp_size == {cfg.dp_size} and "
-                f"attn_cp_size == {attn_cp_size}, "
-                f"moe_dense_tp_size == {overrides['moe_dense_tp_size']}, "
-                f"ep_size == {overrides['ep_size']}, "
-                f"tp_size == {cfg.tp_size}, "
-                f"moe_a2a_backend {overrides['moe_a2a_backend']}, "
-                f"cuda_graph_config[prefill].backend=disabled"
-            )
-    return overrides
-
-
-# Keep in sync with MIMO_V2_MODEL_ARCHS (server_args.py / configs/hf_config.py).
-@_register_for("MiMoV2ForCausalLM", "MiMoV2FlashForCausalLM")
-def _mimo_v2_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-    if cfg.speculative_algorithm == "EAGLE":
-        logger.info("Enable multi-layer EAGLE speculative decoding for MiMoV2 model.")
-        overrides["enable_multi_layer_eagle"] = True
-
-    # On Blackwell "auto" falls through to the triton fused-MoE runner, ~12%
-    # slower at bs=1 decode. FP4 checkpoints use flashinfer_mxfp4 instead.
-    if (
-        get_platform().is_sm100
-        and cfg.moe_runner_backend == "auto"
-        and get_quantization_config(hf_config) == "fp8"
-    ):
-        overrides["moe_runner_backend"] = "flashinfer_trtllm"
-        logger.info("MiMoV2 FP8 on SM100: moe_runner_backend=flashinfer_trtllm.")
-    return overrides
-
-
-@_register_for("MiniMaxM2ForCausalLM")
-def _minimax_m2_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides = {"enable_tf32_matmul": True}
-    logger.info(
-        "Enable TF32 matmul for MiniMaxM2ForCausalLM model to improve gate gemm performance."
-    )
-    if (
-        get_platform().is_sm100
-        and cfg.moe_runner_backend == "auto"
-        and model_config_of(server_args).quantization == "modelopt_fp4"
-    ):
-        overrides["moe_runner_backend"] = "flashinfer_trtllm_routed"
-        logger.info(
-            "Use flashinfer_trtllm_routed as MoE runner backend on SM10X "
-            "for MiniMaxM2ForCausalLM with modelopt_fp4."
-        )
-    return overrides
-
-
-@_register_for("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")
-def _minimax_m3_overrides(server_args: Any, hf_config: Any) -> dict:
-
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-
-    quant_method = get_quantization_config(hf_config)
-    quant_resolved = cfg.quantization
-    if (
-        quant_resolved is None
-        and not server_args._quantization_explicitly_unset
-        and quant_method is not None
-    ):
-        overrides["quantization"] = quant_method
-        quant_resolved = quant_method
-
-    if get_platform().is_hip:
-        if is_attention_backend_not_set(cfg):
-            overrides["attention_backend"] = "triton"
-        if cfg.moe_runner_backend == "auto" and quant_resolved == "mxfp8":
-            overrides["moe_runner_backend"] = "triton"
-        if not envs.USE_ROCM_AITER_ROPE_BACKEND.is_set():
-            envs.USE_ROCM_AITER_ROPE_BACKEND.set("0")
-        aiter_fusion_resolved = cfg.enable_aiter_allreduce_fusion
-        if cfg.ep_size > 1 and cfg.moe_a2a_backend == "none" and aiter_fusion_resolved:
-            logger.warning(
-                "Disable --enable-aiter-allreduce-fusion for MiniMax-M3 "
-                "standard EP on ROCm because the deferred fused all-reduce "
-                "corrupts sparse MoE partial outputs."
-            )
-            overrides["enable_aiter_allreduce_fusion"] = False
-            aiter_fusion_resolved = False
-        # By default MiniMax-M3 on ROCm keeps NCCL all-reduce (custom AR off)
-        # whenever aiter all-reduce fusion is not used. Opting in via
-        # SGLANG_M3_ALLOW_CUSTOM_AR keeps custom all-reduce enabled so the
-        # quick-reduce path (ROCM_QUICK_REDUCE_QUANTIZATION=INT4/INT6/INT8) can
-        # accelerate the large prefill all-reduce.
-        if not aiter_fusion_resolved and not envs.SGLANG_M3_ALLOW_CUSTOM_AR.get():
-            overrides["disable_custom_all_reduce"] = True
-    elif get_platform().is_sm100:
-        if is_attention_backend_not_set(cfg):
-            if (
-                cfg.kv_cache_dtype == "fp8_e4m3"
-                and not envs.SGLANG_DISABLE_M3_FP8_ATTN_GEMM.get()
-            ):
-                # fp8 attention GEMMs activate whenever possible
-                # (m3_fp8_attn_gemm_enabled); only trtllm_mha serves the dense
-                # fp8-q path, so prefer it over fa4 for fp8 KV. The
-                # SGLANG_DISABLE_M3_FP8_ATTN_GEMM kill switch keeps the fa4
-                # default (pre-fp8 behavior).
-                overrides["attention_backend"] = "trtllm_mha"
-            else:
-                overrides["attention_backend"] = "fa4"
-        backend_resolved = overrides.get("attention_backend", cfg.attention_backend)
-        page_resolved = cfg.page_size
-        # fa4 (fmha_sm100) and trtllm_mha both allow the page_size == 128
-        # sparse block MSA needs (trtllm_mha via trtllm-gen's dynamic
-        # tokens-per-page kernels).
-        if page_resolved is None and backend_resolved in ("fa4", "trtllm_mha"):
-            overrides["page_size"] = 128
-            page_resolved = 128
-        if cfg.moe_runner_backend == "auto" and quant_resolved == "mxfp8":
-            overrides["moe_runner_backend"] = "deep_gemm"
-        elif cfg.moe_runner_backend == "auto" and quant_resolved == "modelopt_mixed":
-            overrides["moe_runner_backend"] = "flashinfer_trtllm_routed"
-        logger.info(
-            "MiniMax-M3 on SM100: attention_backend="
-            f"{overrides.get('attention_backend', cfg.attention_backend)}, page_size={page_resolved}, "
-            f"moe_runner_backend={overrides.get('moe_runner_backend', cfg.moe_runner_backend)}."
-        )
-    elif get_platform().is_sm90:
-        if is_attention_backend_not_set(cfg):
-            overrides["attention_backend"] = "fa3"
-        page_resolved = cfg.page_size
-        if (
-            page_resolved is None
-            and overrides.get("attention_backend", cfg.attention_backend) == "fa3"
-        ):
-            overrides["page_size"] = 128
-            page_resolved = 128
-        logger.info(
-            "MiniMax-M3 on Hopper: attention_backend="
-            f"{overrides.get('attention_backend', cfg.attention_backend)}, page_size={page_resolved} "
-            "(MSA is SM100-only; sparse attention runs on the Triton path)."
-        )
-
-    # fp8 attention GEMMs have no opt-in flag: m3_fp8_attn_gemm_enabled
-    # (server_args.py) derives the mode from kv_cache_dtype (fp8_e4m3) +
-    # attention_backend (trtllm_mha) + SM100 at runtime. Surface the
-    # resolution here: warn on fp8_e5m2 (fmha_sm100's variant lookup would
-    # silently dispatch the e4m3 kernel, so e5m2 stays on the widening Triton
-    # path), log when the fp8 GEMM mode is active, and log when the
-    # SGLANG_DISABLE_M3_FP8_ATTN_GEMM kill switch suppresses it.
-    if cfg.kv_cache_dtype == "fp8_e5m2":
-        logger.warning(
-            "MiniMax-M3 with kv_cache_dtype fp8_e5m2: fp8 attention GEMMs stay "
-            "DISABLED (fmha_sm100's variant lookup would silently dispatch the "
-            "e4m3 kernel for e5m2); sparse attention runs on the widening "
-            "Triton path. Use --kv-cache-dtype fp8_e4m3 for fp8 attention GEMMs."
-        )
-    elif (
-        cfg.kv_cache_dtype == "fp8_e4m3"
-        and overrides.get("attention_backend", cfg.attention_backend) == "trtllm_mha"
-        and get_platform().is_sm100
-    ):
-        if envs.SGLANG_DISABLE_M3_FP8_ATTN_GEMM.get():
-            logger.info(
-                "MiniMax-M3 fp8 attention GEMMs DISABLED by "
-                "SGLANG_DISABLE_M3_FP8_ATTN_GEMM: bf16 indexer + widening "
-                "Triton sparse path, bf16 q; dense layers keep trtllm_mha's "
-                "fp8 KV cache."
-            )
-        else:
-            logger.info(
-                "MiniMax-M3 fp8 attention GEMMs active (kv_cache_dtype fp8_e4m3 + "
-                "trtllm_mha on SM100): fp8 main/index KV, fp8-cast q, fp8 "
-                "sparse/MSA kernels. Set SGLANG_DISABLE_M3_FP8_ATTN_GEMM=1 to "
-                "force the pre-fp8 numerics."
-            )
-
-    moe_runner_resolved = overrides.get("moe_runner_backend", cfg.moe_runner_backend)
-    if quant_resolved is None and moe_runner_resolved in ("auto", "deep_gemm"):
-        if moe_runner_resolved == "deep_gemm":
-            logger.warning(
-                "MiniMax-M3: the deep_gemm MoE runner produces corrupted output "
-                "on bf16 full weights; overriding --moe-runner-backend to 'triton'."
-            )
-        overrides["moe_runner_backend"] = "triton"
-
-    return overrides
-
-
-@_register_for(
-    "Gemma2ForCausalLM",
-    "Gemma3ForCausalLM",
-    "Gemma3ForConditionalGeneration",
-    "Gemma3nForCausalLM",
-    "Gemma3nForConditionalGeneration",
-)
-def _gemma2_gemma3_overrides(server_args: Any, hf_config: Any) -> dict:
-    # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with gemma2 model.
-    # It failed at this test: https://github.com/sgl-project/sglang/actions/runs/16255155597/job/45890331952#step:4:736
-    logger.warning(
-        f"Disable hybrid SWA memory for {hf_config.architectures[0]} as it is not yet supported."
-    )
-    return {"disable_hybrid_swa_memory": True}
-
-
-@_register_for("Exaone4ForCausalLM", "ExaoneMoEForCausalLM")
-def _exaone_overrides(server_args: Any, hf_config: Any) -> dict:
-    if hf_config.sliding_window_pattern is not None:
-        logger.warning(
-            f"Disabling hybrid SWA memory for {hf_config.architectures[0]} as it is not yet supported."
-        )
-        return {"disable_hybrid_swa_memory": True}
-    return {}
-
-
-@_register_for("GptOssForCausalLM")
-def _gpt_oss_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-    # Set attention backend for GPT-OSS
-    if is_attention_backend_not_set(cfg):
-        if get_platform().is_sm100:
-            overrides["attention_backend"] = "trtllm_mha"
-        elif get_platform().is_sm90:
-            overrides["attention_backend"] = "fa3"
-        elif is_cpu() and get_platform().has_amx:
-            overrides["attention_backend"] = "intel_amx"
-        elif get_platform().is_xpu:
-            overrides["attention_backend"] = "intel_xpu"
-        elif get_platform().is_hip:
-            overrides["attention_backend"] = "aiter"
-        elif not (is_mps() and use_mlx()):
-            # Exempt MLX only -- it owns attention in its own runner.  macOS
-            # without MLX still falls through to triton and fails fast below,
-            # rather than landing on torch_native (no sliding window, no sinks).
-            overrides["attention_backend"] = "triton"
-    if get_platform().is_xpu:
-        # Check for bf16 dtype on Intel XPU. Reads the pristine dtype request,
-        # which equals the legacy mid-branch read: dtype had no earlier writer
-        # for this arch.
-        if cfg.dtype == "auto":
-            logger.warning(
-                "GptOssForCausalLM on Intel XPU currently supports bfloat16 dtype only"
-            )
-        elif cfg.dtype not in ["bfloat16"]:
-            raise NotImplementedError(
-                f"GptOssForCausalLM on Intel XPU only supports bfloat16 dtype, "
-                f"but got '{cfg.dtype}'. Please use --dtype bfloat16 or remove --dtype to use auto."
-            )
-    quantization_config = getattr(hf_config, "quantization_config", None)
-    is_mxfp4_quant_format = (
-        quantization_config is not None
-        and quantization_config.get("quant_method") == "mxfp4"
-    )
-    if is_mxfp4_quant_format:
-        # use bf16 for mxfp4 triton kernels
-        overrides["dtype"] = "bfloat16"
-    if cfg.moe_runner_backend == "auto":
-
-        if get_platform().is_sm100 and is_mxfp4_quant_format:
-            overrides["moe_runner_backend"] = "flashinfer_mxfp4"
-            logger.warning(
-                "Detected SM100 and MXFP4 quantization format for GPT-OSS model, enabling FlashInfer MXFP4 MOE kernel."
-            )
-        elif get_platform().is_sm120 and is_mxfp4_quant_format:
-            overrides["moe_runner_backend"] = "flashinfer_mxfp4"
-            logger.warning(
-                "Detected SM120 and MXFP4 quantization format for GPT-OSS model, "
-                "enabling FlashInfer CUTLASS MXFP4 MOE kernel."
-            )
-        elif (
-            get_platform().is_hip and envs.SGLANG_USE_AITER.get()
-        ) and is_mxfp4_quant_format:
-            overrides["moe_runner_backend"] = "auto"
-            logger.warning(
-                "Detected ROCm and MXFP4 quantization format for GPT-OSS model, enabling aiter MXFP4 MOE kernel."
-            )
-            ## The AITER MXFP4 fused-MoE path for GPT-OSS expects the
-            ## SEPARATED gate/up tile layout (matches the
-            ## `gptoss_fp4_tuned_fmoe.csv` flydsl entries and the
-            ## Mxfp4MoEMethod weight shuffle). Other AITER MXFP4
-            ## callers default to INTERLEAVE; opt this path out
-            ## unless the user explicitly overrode it.
-            # envs.SGLANG_USE_AITER_MOE_GU_ITLV.set(False)
-        elif get_platform().is_hip and envs.SGLANG_USE_AITER.get():
-            # For GPT-OSS bf16 on ROCm with aiter, use triton backend
-            # because aiter CK kernel doesn't support all GEMM dimensions
-            overrides["moe_runner_backend"] = "triton"
-            logger.warning(
-                "Detected ROCm with SGLANG_USE_AITER for GPT-OSS bf16 model, using triton MOE kernel."
-            )
-        elif get_platform().is_musa and envs.SGLANG_DEEPEP_BF16_DISPATCH.get():
-            overrides["moe_runner_backend"] = "deep_gemm"
-            logger.warning(
-                "Detected MUSA with SGLANG_DEEPEP_BF16_DISPATCH for bf16 model, using deep_gemm kernel."
-            )
-        elif (
-            cfg.ep_size == 1
-            and is_triton_kernels_available()
-            and cfg.quantization is None
-            and not (is_cpu() and get_platform().has_amx)
-        ):
-            # The triton_kernels package segfaults on Blackwell (B200)
-            # with NVIDIA driver >= 595. Fall back to triton backend.
-            if get_platform().is_blackwell and get_nvidia_driver_version() >= (595,):
-                overrides["moe_runner_backend"] = "triton"
-                logger.warning(
-                    "Detected GPT-OSS model on Blackwell with driver >= 595, "
-                    "using triton MOE kernel to avoid triton_kernels SIGSEGV."
-                )
-            else:
-                overrides["moe_runner_backend"] = "triton_kernel"
-                logger.warning(
-                    "Detected GPT-OSS model, enabling triton_kernels MOE kernel."
-                )
-    return overrides
-
-
-# Keep in sync with LLAMA4_MODEL_ARCHS (server_args.py).
-@_register_for("Llama4ForConditionalGeneration", "Llama4ForCausalLM")
-def _llama4_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if cfg.device == "cpu":
-        return {}
-    overrides: Dict[str, Any] = {}
-    # Auto-select attention backend for Llama4 if not specified
-    if cfg.attention_backend is None:
-        if get_platform().is_sm100:
-            backend, platform = "trtllm_mha", "sm100"
-        elif get_platform().is_sm90:
-            backend, platform = "fa3", "sm90"
-        elif get_platform().is_hip:
-            backend, platform = "aiter", "hip"
-        elif cfg.device == "xpu":
-            backend, platform = "intel_xpu", "xpu"
-        else:
-            backend, platform = "triton", "other platforms"
-        logger.warning(
-            f"Use {backend} as attention backend on {platform} for Llama4 model"
-        )
-        overrides["attention_backend"] = backend
-    if get_platform().is_sm100 and cfg.moe_runner_backend == "auto":
-        if cfg.quantization in {"fp8", "modelopt_fp8"}:
-            overrides["moe_runner_backend"] = "flashinfer_trtllm"
-            logger.info(
-                "Use flashinfer_trtllm as MoE runner backend on SM100 for Llama4"
-            )
-    return overrides
-
-
-@_register_for(
-    "Gemma4ForConditionalGeneration",
-    "Gemma4ForCausalLM",
-    "Gemma4UnifiedForConditionalGeneration",
-)
-def _gemma4_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-    default_attention_backend = "trtllm_mha" if get_platform().is_sm100 else "triton"
-    if is_attention_backend_not_set(cfg):
-        logger.info(
-            f"Use {default_attention_backend} as default attention backend for Gemma4"
-        )
-        overrides["attention_backend"] = default_attention_backend
-    # If only one split backend is set, keep the other side on a
-    # Gemma4-compatible fallback instead of letting generic backend selection
-    # choose an unsupported backend later.
-    elif cfg.attention_backend is None:
-        overrides["attention_backend"] = default_attention_backend
-    if get_platform().is_sm100 and cfg.moe_runner_backend == "auto":
-        if model_config_of(server_args).quantization == "modelopt_fp4":
-            overrides["quantization"] = "modelopt_fp4"
-            overrides["moe_runner_backend"] = "flashinfer_trtllm"
-            logger.info(
-                "Use flashinfer_trtllm as MoE runner backend on "
-                "SM100 for Gemma-4 (modelopt_fp4)"
-            )
-    return overrides
-
-
-@_register_for("MossVLForConditionalGeneration")
-def _moss_vl_overrides(server_args: Any, hf_config: Any) -> dict:
-    overrides: Dict[str, Any] = {}
-    if is_attention_backend_not_set(resolving_view(server_args)):
-        overrides["prefill_attention_backend"] = "flashinfer"
-        logger.info("Use flashinfer as default prefill attention backend for Moss-VL")
-    prefill_backend = (
-        overrides.get("prefill_attention_backend")
-        or attention_backends_of(resolved_view(server_args))[0]
-    )
-    assert prefill_backend == "flashinfer", (
-        "MossVLForConditionalGeneration requires flashinfer prefill "
-        "attention backend for cross-attention custom mask support."
-    )
-    return overrides
-
-
-@_register_for("MiniCPMForCausalLM", "MiniCPMSALAForCausalLM")
-def _minicpm_sala_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if cfg.enable_dp_attention:
-        raise ValueError("MiniCPM does not support DP attention")
-    has_sparse_attention = getattr(hf_config, "has_minicpm_sparse_attention", False)
-    has_hybrid_attention = has_sparse_attention or getattr(
-        hf_config, "has_lightning_layers", False
-    )
-    overrides: Dict[str, Any] = {}
-    if has_hybrid_attention:
-        if cfg.enable_hierarchical_cache:
-            raise ValueError("MiniCPM SALA does not support hierarchical cache")
-        overrides["disable_radix_cache"] = True
-    if envs.SGLANG_MINICPM_FORCE_DENSE.get():
-        dense_backends = {
-            "minicpm_flashattn": ("fa4" if get_platform().is_blackwell else "fa3"),
-            "minicpm_flashinfer": "flashinfer",
-        }
-        # Literal keys keep the written-field set statically derivable; a loop
-        # variable hides it from the census in test_chain_read_ratchet.py.
-        dense_attention = dense_backends.get(cfg.attention_backend)
-        if dense_attention is not None:
-            overrides["attention_backend"] = dense_attention
-        dense_prefill = dense_backends.get(cfg.prefill_attention_backend)
-        if dense_prefill is not None:
-            overrides["prefill_attention_backend"] = dense_prefill
-        dense_decode = dense_backends.get(cfg.decode_attention_backend)
-        if dense_decode is not None:
-            overrides["decode_attention_backend"] = dense_decode
-    elif has_sparse_attention:
-        uses_sparse_backend = is_attention_backend_not_set(cfg) or any(
-            backend in ("minicpm_flashattn", "minicpm_flashinfer")
-            for backend in (
-                cfg.attention_backend,
-                cfg.prefill_attention_backend,
-                cfg.decode_attention_backend,
-            )
-        )
-        if uses_sparse_backend and cfg.disaggregation_mode != "null":
-            raise ValueError(
-                "MiniCPM sparse attention does not support PD disaggregation"
-            )
-        if is_attention_backend_not_set(cfg):
-            overrides["attention_backend"] = (
-                "minicpm_flashinfer"
-                if get_platform().is_blackwell
-                else "minicpm_flashattn"
-            )
-    return overrides
-
-
-@_register_for("MiniCPMV4_6ForConditionalGeneration")
-def _minicpm_v4_6_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if get_platform().is_sm100 and cfg.attention_backend is None:
-        return {"attention_backend": "triton"}
-    return {}
-
-
-@_register_for(
-    "FalconH1ForCausalLM", "JetNemotronForCausalLM", "JetVLMForConditionalGeneration"
-)
-def _falcon_h1_jet_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if get_platform().is_sm100 and cfg.attention_backend is None:
-        return {"attention_backend": "triton"}
-    return {}
-
-
-@_register_for("GraniteMoeHybridForCausalLM")
-def _granite_moe_hybrid_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    has_mamba = any(
-        layer_type == "mamba" for layer_type in getattr(hf_config, "layer_types", [])
-    )
-    if has_mamba and get_platform().is_sm100 and cfg.attention_backend is None:
-        return {"attention_backend": "flashinfer"}
-    return {}
-
-
-@_register_for("Lfm2ForCausalLM", "Lfm2MoeForCausalLM")
-def _lfm2_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if get_platform().is_sm100 and cfg.attention_backend is None:
-        return {"attention_backend": "flashinfer"}
-    return {}
-
-
-@_register_for("DeepseekV4ForCausalLM")
-def _deepseek_v4_overrides(server_args: Any, hf_config: Any) -> dict:
-    """DeepSeek V4 attention/page/window/MoE-runner defaults (from
-    arg_groups/deepseek_v4_hook.py). The kv-cache dtype and NPU split-backend
-    writes, the max_running_requests fill and the validations stay in the
-    hook at its legacy slot."""
-    cfg = resolving_view(server_args)
-    from sglang.srt.server_args import ServerArgs
-
-    model_arch = hf_config.architectures[0]
-    overrides: Dict[str, Any] = {"attention_backend": "dsv4"}
-
-    page_size = 256
-    if cfg.device == "npu":
-        # NPU keeps the device-aware "dsv4" backend (the registry routes it to
-        # the Ascend V4 subclass); only the pool geometry / dtype differ.
-        # set_default_server_args() pins all three backends to "ascend" for
-        # generic NPU models; override that here so V4 stays consistently on
-        # dsv4.
-        page_size = 128
-        overrides["prefill_attention_backend"] = "dsv4"
-        overrides["decode_attention_backend"] = "dsv4"
-    overrides["page_size"] = page_size
-    logger.info(
-        f"Use dsv4 attention backend for {model_arch}, setting page_size to {page_size}."
-    )
-
-    if cfg.swa_full_tokens_ratio == ServerArgs.swa_full_tokens_ratio:
-        overrides["swa_full_tokens_ratio"] = 0.1
-        logger.info(f"Setting swa_full_tokens_ratio to 0.1 for {model_arch}.")
-
-    if cfg.moe_runner_backend == "auto":
-        model_config = model_config_of(server_args)
-        # nvidia/DeepSeek-V4-Pro-NVFP4 uses the routed TRT-LLM runner.
-        if model_config.nvfp4_moe_meta is not None:
-            overrides["moe_runner_backend"] = "flashinfer_trtllm_routed"
-            logger.info(
-                "Use flashinfer_trtllm_routed as MoE runner backend for "
-                f"{model_arch} hybrid FP8+NVFP4 checkpoint."
-            )
-        elif (
-            cfg.device == "cuda"
-            and not get_platform().is_hip
-            and cfg.moe_a2a_backend == "none"
-            and not envs.SGLANG_DSV4_FP4_DEQUANT.get()
-            and model_config.is_fp4_experts
-            and (
-                get_platform().is_sm90
-                or get_platform().is_sm100
-                or get_platform().is_sm120
-            )
-        ):
-            overrides["moe_runner_backend"] = "flashinfer_mxfp4"
-            logger.info(
-                "Use flashinfer_mxfp4 as MoE runner backend for " f"{model_arch}."
-            )
-    return overrides
-
-
-@_register_for(
-    "InklingForConditionalGeneration",
-    "InklingForConditionalGenerationMTP",
-)
-def _inkling_overrides(server_args: Any, hf_config: Any) -> dict:
-    """Inkling architecture defaults: SWA / mamba KV-pool ratios tuned for the
-    hybrid-SWA layout, the extra-buffer mamba strategy, and the unified radix
-    tree (which Inkling requires — models/inkling.py asserts it). The full-graph
-    prefill default is set separately (inline, before cuda-graph resolution) —
-    see ServerArgs.__post_init__ / _apply_inkling_prefill_cuda_graph_default. The
-    server-arg defaults each yield to an explicit user value (compared against
-    the ServerArgs class default); the prefill declaration is materialized
-    before _parse_cuda_graph_config folds cuda_graph_backend_prefill into
-    prefill.backend, and an explicit --cuda-graph-backend-prefill /
-    --disable-prefill-cuda-graph still wins. The unified-radix env write follows
-    the MiniMax-M3 handler precedent (env is not a resolvable server-arg)."""
-    cfg = resolving_view(server_args)
-    from sglang.srt.server_args import ServerArgs
-
-    overrides: Dict[str, Any] = {}
-    # NOTE: the full-graph prefill default is NOT set here. cuda-graph config is
-    # resolved in __post_init__ before declarations are materialized, so a
-    # cuda_graph_backend_prefill declared here lands too late (the breakable
-    # default would already have been auto-disabled for this multimodal arch).
-    # It is set inline before _handle_cuda_graph_config instead.
-    if cfg.swa_full_tokens_ratio == ServerArgs.swa_full_tokens_ratio:
-        overrides["swa_full_tokens_ratio"] = 0.1
-    if cfg.mamba_full_memory_ratio == ServerArgs.mamba_full_memory_ratio:
-        overrides["mamba_full_memory_ratio"] = 0.1
-    # Inkling requires the extra-buffer mamba strategy (inkling.py asserts
-    # enable_mamba_extra_buffer()); the generic "auto" resolution does not cover
-    # Inkling, so pin it here. Yields to an explicit --mamba-scheduler-strategy.
-    if cfg.mamba_radix_cache_strategy == ServerArgs.mamba_radix_cache_strategy:
-        overrides["mamba_radix_cache_strategy"] = "extra_buffer"
-    # Inkling attention runs only on the fa4 (Blackwell) or triton backends --
-    # models/inkling_common/attn.py asserts attention_backend in {fa4, triton}.
-    # The generic resolver would otherwise pick trtllm_mha (SM100) / fa3
-    # (Hopper), so a bare launch fails on the first attention forward. Pin a
-    # supported default when the user left every attention-backend flag unset
-    # (mirrors the MiniMax-M3 SM100 fa4-default above); an explicit
-    # --attention-backend / --prefill/decode-attention-backend still wins.
-    if is_attention_backend_not_set(cfg):
-        inkling_attn_backend = "fa4" if get_platform().is_sm100 else "triton"
-        overrides["attention_backend"] = inkling_attn_backend
-        logger.info(
-            f"Use {inkling_attn_backend} as the attention backend for Inkling "
-            "(requires fa4 or triton)."
-        )
-    envs.SGLANG_ENABLE_UNIFIED_RADIX_TREE.set(True)
-    return overrides
-
-
-@_register_for("NemotronHForCausalLM", "NemotronHPuzzleForCausalLM")
-def _nemotron_h_overrides(server_args: Any, hf_config: Any) -> dict:
-    """NemotronH quantization / MoE runner / attention backend defaults
-    (absorbed from the retired arg_groups/nemotron_h_hook.py; the mamba radix
-    cache handling and the triton-backend assert stay in the arch branch)."""
-    cfg = resolving_view(server_args)
-    model_arch = hf_config.architectures[0]
-    model_config = model_config_of(server_args)
-    overrides: Dict[str, Any] = {}
-
-    is_modelopt = model_config.quantization in [
-        "modelopt",
-        "modelopt_fp8",
-        "modelopt_fp4",
-        "modelopt_mixed",
-    ]
-    quantization = cfg.quantization
-    if is_modelopt:
-        assert model_config.hf_config.mlp_hidden_act == "relu2"
-        if model_config.quantization == "modelopt":
-            quant_algo = model_config.hf_config.quantization_config["quant_algo"]
-            if quant_algo == "MIXED_PRECISION":
-                quantization = "modelopt_mixed"
-            else:
-                quantization = (
-                    "modelopt_fp4" if quant_algo == "NVFP4" else "modelopt_fp8"
-                )
-        else:
-            quantization = model_config.quantization
-        overrides["quantization"] = quantization
-
-    has_w4a16_moe_layers = False
-    if is_modelopt and quantization == "modelopt_mixed":
-        has_w4a16_moe_layers = any(
-            info.get("quant_algo") == "W4A16_NVFP4" and ".experts." in name
-            for name, info in hf_config.quantization_config.get(
-                "quantized_layers", {}
-            ).items()
-        )
-
-    if has_w4a16_moe_layers:
-        if cfg.moe_a2a_backend != "none":
-            raise ValueError("W4A16_NVFP4 MoE layers require --moe-a2a-backend=none.")
-        if cfg.moe_runner_backend not in ("auto", "marlin"):
-            raise ValueError(
-                "W4A16_NVFP4 MoE layers require --moe-runner-backend=marlin."
-            )
-        if cfg.moe_runner_backend == "auto":
-            overrides["moe_runner_backend"] = "marlin"
-            logger.info(
-                "Use marlin as MoE runner backend for "
-                f"{model_arch} with W4A16_NVFP4 MoE layers"
-            )
-    elif (is_modelopt or model_config.quantization is None) and (
-        cfg.moe_runner_backend == "auto"
-    ):
-        if get_platform().is_sm100 and cfg.moe_a2a_backend == "none":
-            overrides["moe_runner_backend"] = "flashinfer_trtllm"
-            logger.info(
-                f"Use flashinfer_trtllm as MoE runner backend on sm100 for {model_arch}"
-            )
-        elif (
-            (
-                model_config.quantization in ("modelopt_fp4", "modelopt_mixed")
-                or quantization == "modelopt_fp4"
-            )
-            and get_platform().is_cuda
-            and (8, 0) <= get_platform().device_capability < (10, 0)
-        ):
-            overrides["moe_runner_backend"] = "marlin"
-            logger.info(
-                "Use marlin as MoE runner backend on SM80-SM90 for "
-                f"{model_arch} {model_config.quantization}"
-            )
-        else:
-            overrides["moe_runner_backend"] = "flashinfer_cutlass"
-
-    if get_platform().is_blackwell and is_attention_backend_not_set(cfg):
-        if cfg.speculative_algorithm is not None:
-            speculative_algorithm = cfg.speculative_algorithm.upper()
-            if get_platform().is_sm100 and cfg.speculative_eagle_topk in (
-                None,
-                1,
-            ):
-                overrides["attention_backend"] = "trtllm_mha"
-                if cfg.page_size is None:
-                    overrides["page_size"] = 64
-                if cfg.mamba_radix_cache_strategy == "auto":
-                    overrides["mamba_radix_cache_strategy"] = "extra_buffer"
-                if (
-                    cfg.speculative_draft_attention_backend is None
-                    and speculative_algorithm in ("EAGLE", "NEXTN", "DSPARK")
-                ):
-                    overrides["speculative_draft_attention_backend"] = "trtllm_mha"
-            else:
-                overrides["attention_backend"] = "triton"
-                if (
-                    cfg.speculative_draft_attention_backend is None
-                    and speculative_algorithm in ("EAGLE", "NEXTN", "DFLASH", "DSPARK")
-                ):
-                    overrides["speculative_draft_attention_backend"] = "flashinfer"
-        elif get_platform().is_sm100:
-            overrides["attention_backend"] = "trtllm_mha"
-    return overrides
-
-
-@_register_for(
-    "Qwen3NextForCausalLM",
-    "Qwen3_5MoeForConditionalGeneration",
-    "InternS2PreviewForConditionalGeneration",
-    "InternS2MobiusForConditionalGeneration",
-    "Qwen3_5ForConditionalGeneration",
-)
-def _qwen3_5_hybrid_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if not get_platform().is_sm100 or cfg.attention_backend is not None:
-        return {}
-    sm100_default_attn_backend = "triton"
-    # trtllm_mha requires speculative_eagle_topk == 1 and page_size > 1.
-    # get_default_attn_backend handles the eagle_topk check.
-    # There is only one case where page_size=1 is required,
-    # which is when radix cache is enabled and both extra_buffer
-    # and spec decoding are disabled.
-    default_attn_backend = get_default_attn_backend(
-        server_args,
-        use_mla_backend=use_mla_backend(server_args),
-        model_config=model_config_of(server_args),
-    )
-    # The mamba radix-cache pass runs before this dispatch: read the
-    # declared strategy through the view (the legacy branch observed the
-    # already-written field here).
-    if default_attn_backend == "trtllm_mha" and not (
-        not mamba_extra_buffer_of(resolved_view(server_args))
-        and not cfg.disable_radix_cache
-        and cfg.speculative_algorithm is None
-    ):
-        sm100_default_attn_backend = "trtllm_mha"
-    return {
-        "attention_backend": sm100_default_attn_backend,
-        "page_size": 64 if sm100_default_attn_backend == "trtllm_mha" else 1,
-    }
-
-
-@_register_for("InternS2MobiusForConditionalGeneration")
-def _interns2_mobius_baseline_overrides(server_args: Any, hf_config: Any) -> dict:
-    """Select the only MoE runner validated for the 2,560-expert baseline."""
-    cfg = resolving_view(server_args)
-    if cfg.moe_runner_backend == "auto":
-        return {"moe_runner_backend": "triton_kernel"}
-    return {}
-
-
-@_register_for("Qwen3VLForConditionalGeneration")
-def _qwen3vl_overrides(server_args: Any, hf_config: Any) -> dict:
-
-    cfg = resolving_view(server_args)
-    if (
-        get_platform().is_hip
-        and envs.SGLANG_USE_AITER_UNIFIED_ATTN.get()
-        and cfg.page_size is None
-    ):
-        logger.info(
-            "Setting page_size=16 for aiter unified attention on Qwen3VLForConditionalGeneration."
-        )
-        return {"page_size": 16}
-    return {}
-
-
-@_register_for(
-    "Qwen3MoeForCausalLM",
-    "Qwen3VLMoeForConditionalGeneration",
-    "Qwen3NextForCausalLM",
-    "Qwen3_5MoeForConditionalGeneration",
-    "InternS2PreviewForConditionalGeneration",
-    "Qwen3_5ForConditionalGeneration",
-)
-def _qwen3_moe_family_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-    if get_platform().is_sm100:
-        quant_method = get_quantization_config(hf_config)
-        quantization = cfg.quantization
-        if (
-            quantization is None
-            and not server_args._quantization_explicitly_unset
-            and quant_method is not None
-        ):
-            overrides["quantization"] = quant_method
-            quantization = quant_method
-        if (
-            (quantization in ("fp8", "modelopt_fp4") or quantization is None)
-            and cfg.moe_a2a_backend == "none"
-            and cfg.moe_runner_backend == "auto"
-        ):
-            overrides["moe_runner_backend"] = "flashinfer_trtllm"
-            logger.info(
-                "Use flashinfer_trtllm as MoE runner backend on sm100 for "
-                f"{hf_config.architectures[0]}"
-            )
-    return overrides
-
-
-@_register_for("Glm4MoeForCausalLM")
-def _glm4_moe_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-    if get_platform().is_sm100:
-        quantization_config = getattr(hf_config, "quantization_config", None)
-        quant_method = (
-            quantization_config.get("quant_method")
-            if quantization_config is not None
-            else None
-        )
-        quantization = cfg.quantization
-        if (
-            quantization is None
-            and not server_args._quantization_explicitly_unset
-            and quant_method is not None
-        ):
-            overrides["quantization"] = quant_method
-            quantization = quant_method
-        if (
-            quantization in {"modelopt_fp4", None}
-            and cfg.moe_a2a_backend == "none"
-            and cfg.moe_runner_backend == "auto"
-        ):
-            overrides["moe_runner_backend"] = "flashinfer_trtllm"
-            logger.info(
-                "Use flashinfer_trtllm as MoE runner backend on sm100 for Glm4MoeForCausalLM"
-            )
-    logger.info(
-        "Enable TF32 matmul for Glm4MoeForCausalLM model to improve gate gemm performance."
-    )
-    overrides["enable_tf32_matmul"] = True
-    return overrides
-
-
-@_register_for("Olmo2ForCausalLM")
-def _olmo2_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    overrides: Dict[str, Any] = {}
-    # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with Olmo3 model.
-    logger.warning(
-        f"Disabling hybrid SWA memory for {hf_config.architectures[0]} as it is not yet supported."
-    )
-    overrides["disable_hybrid_swa_memory"] = True
-    if cfg.attention_backend is None:
-        if get_platform().is_cuda and get_platform().is_sm100:
-            overrides["attention_backend"] = "trtllm_mha"
-        elif get_platform().is_cuda and get_platform().device_sm >= 80:
-            overrides["attention_backend"] = "fa3"
-        else:
-            overrides["attention_backend"] = "triton"
-    return overrides
+# Importing the package is what registers the per-model declarations.
+import sglang.srt.arg_groups.model_overrides  # noqa: F401
 
 
 @register_model_override_predicate(
@@ -1797,8 +515,7 @@ _MAMBA_RADIX_CACHE_ARCHS = frozenset(
 )
 
 # Architectures that support the extra_buffer mamba radix cache strategy.
-# Single source of truth: ServerArgs._support_mamba_cache_extra_buffer
-# delegates here.
+# The single source of truth; `supports_mamba_cache_extra_buffer` reads it.
 _MAMBA_EXTRA_BUFFER_ARCHS = frozenset(
     {
         "KimiLinearForCausalLM",
@@ -2211,15 +928,6 @@ def _deepseek_v4_kv_cache_dtype(view: Any) -> dict:
     return {}
 
 
-@_register_for("MuseGlimmerForConditionalGeneration", "MuseGlimmerForCausalLM")
-def _muse_glimmer_fp4_gemm_runner_overrides(server_args: Any, hf_config: Any) -> dict:
-    cfg = resolving_view(server_args)
-    if get_platform().is_sm120 and cfg.fp4_gemm_runner_backend == "auto":
-        logger.info("Use marlin as FP4 GEMM runner backend on SM120 for Muse Glimmer")
-        return {"fp4_gemm_runner_backend": "marlin"}
-    return {}
-
-
 @register_post_process
 def _sparse_head_overlap_disable(view: Any) -> dict:
 
@@ -3034,155 +1742,11 @@ def _hrm_text_attention_force(view: Any) -> dict:
     return {"attention_backend": "triton"}
 
 
-def record_of(view: Any) -> Any:
-    """The record a view reads through.
-
-    For the few helpers a view cannot serve: `get_default_attn_backend` reads
-    through *both* overlays, so it needs the record the two views are built
-    from rather than either one of them.
-    """
-    return object.__getattribute__(view, "_server_args")
-
-
-def is_attention_backend_not_set(cfg: Any):
-    """None of the three attention backends has been decided yet.
-
-    Takes the view rather than the record: every read is a view read, and the
-    callers that hold a view (the override providers) would otherwise have to
-    reach back through it for a record.
-    """
-    return (
-        cfg.attention_backend is None
-        and cfg.prefill_attention_backend is None
-        and cfg.decode_attention_backend is None
-    )
-
-
-def get_default_attn_backend(server_args: Any, use_mla_backend: bool, model_config):
-    """
-    Auto select the fastest attention backend.
-
-    1. Models with MHA Architecture (e.g: Llama, QWen)
-        1.1 We will turn on FA3 on hopper unless user use spec decode with topk > 1 or page_size > 1.
-        1.2 Use trtllm_mha for SM100/SM103 (Blackwell B200/GB200/B300) excluding spec with topk > 1.
-           Note: trtllm_mha does not support SM120, which will fall back to flashinfer.
-        1.3 In other cases, we will use flashinfer if available, otherwise use triton.
-    2. Models with MLA Architecture and using FA3
-        2.1 We will use FA3 backend on hopper.
-        2.2 We will use Flashinfer backend on blackwell.
-        2.3 Otherwise, we will use triton backend.
-    """
-    cfg = resolving_view(server_args)
-    # OOT platforms provide their own default attention backend.
-    if current_platform.is_out_of_tree():
-        return current_platform.get_default_attention_backend()
-
-    # Whisper requires flashinfer for cross-attention CUDA graph support.
-    if "WhisperForConditionalGeneration" in (
-        model_config.hf_config.architectures or []
-    ):
-        return "flashinfer"
-
-    if not use_mla_backend:
-        # MHA architecture
-
-        if get_platform().is_hopper_with_cuda_12_3 and is_no_spec_infer_or_topk_one(
-            resolved_view(server_args)
-        ):
-            # Note: flashinfer 0.6.1 caused performance regression on Hopper attention kernel
-            # Before the kernel is fixed, we choose fa3 as the default backend on Hopper MHA
-            # ref: https://github.com/sgl-project/sglang/issues/17411
-            return "fa3"
-        elif (
-            get_platform().is_sm100
-            and is_no_spec_infer_or_topk_one(resolved_view(server_args))
-            and (
-                cfg.speculative_algorithm is None
-                or cfg.speculative_eagle_topk is not None
-            )
-        ):
-            # trtllm_mha requires equal K/V row widths; fa4 carries
-            # v_head_dim through.
-            if model_config.has_asymmetric_kv:
-                return "fa4"
-            return "trtllm_mha"
-        elif get_platform().is_hip:
-            return "aiter"
-        elif is_mps():
-            return "torch_native"
-        else:
-            # FlashInfer does not support attention sinks.
-            if get_platform().has_flashinfer and not model_config.has_attention_sinks:
-                return "flashinfer"
-            return "triton"
-    else:
-        # MLA architecture
-        if get_platform().is_hopper_with_cuda_12_3:
-            return "fa3"
-        elif get_platform().is_sm100:
-            return "flashinfer"
-        elif get_platform().is_hip:
-            head_num = model_config.get_num_kv_heads(cfg.tp_size)
-            # TODO current aiter only support head number 16 or 128 head number
-            if head_num == 128 or head_num == 16:
-                return "aiter"
-            else:
-                return "triton"
-        elif is_mps():
-            return "torch_native"
-        else:
-            return "triton"
-
-
-def use_mla_backend(server_args: Any):
-    from sglang.srt.configs.model_config import AttentionArch
-
-    model_config = model_config_of(server_args)
-    return model_config.attention_arch == AttentionArch.MLA
-
-
 def should_report_expert_balancedness(server_args: Any) -> bool:
     cfg = resolving_view(server_args)
     return cfg.expert_balancedness_report_mode != "off"
 
 
-def model_config_of(server_args: Any):
-    """The model configuration this record describes, built once and memoised.
-
-    Takes a view as readily as the record: a view is a read overlay of one
-    record, the memo has to live on that record either way, and the callers
-    that hold a view would otherwise all have to unwrap it themselves.
-    """
-    if isinstance(server_args, (ResolvedView, ResolvingConfig)):
-        server_args = record_of(server_args)
-    # Lazy init to avoid circular import
-    cfg = resolving_view(server_args)
-    from sglang.srt.configs.model_config import ModelConfig
-
-    memo = getattr(server_args, "_model_config", None)
-    if memo is not None:
-        # The key is the path this record carried when the cache was
-        # filled. The GGUF and ModelScope handlers declare a different
-        # `model_path`, and a configuration built before them describes
-        # another checkpoint. `ModelConfig` re-points its own `model_path`
-        # at the local pull directory when the weights sit behind an
-        # object-store URI, so its field is not the key. A configuration a
-        # fixture supplied carries no key and is handed back as it is.
-        built_from = getattr(server_args, "_model_config_built_from", None)
-        if built_from is None or built_from == cfg.model_path:
-            return memo
-
-    model_config = ModelConfig.from_server_args(server_args)
-    server_args._model_config = model_config
-    server_args._model_config_built_from = cfg.model_path
-    if model_config.is_hybrid_swa:
-        logger.info(
-            "Hybrid SWA model detected. architectures=%s",
-            model_config.hf_config.architectures,
-        )
-    return model_config
-
-
 def post_capture_kv_sizing_planned(server_args: Any) -> bool:
     """Whether the mem_fraction heuristic may skip the graph reserve; must be
     False for any config the runtime won't post-capture-size, else it gets an
diff --git a/python/sglang/srt/arg_groups/serving_hook.py b/python/sglang/srt/arg_groups/serving_hook.py
index 2cf7302f2..fe7713124 100644
--- a/python/sglang/srt/arg_groups/serving_hook.py
+++ b/python/sglang/srt/arg_groups/serving_hook.py
@@ -910,3 +910,32 @@ def handle_multimodal_feature_transport(server_args: Any):
     envs.SGLANG_USE_CUDA_IPC_TRANSPORT.set(
         "1" if requested_transport == "cuda_ipc" else "0"
     )
+
+
+_ssl_verify_warned = False
+
+
+def ssl_verify_of(cfg: Any):
+    """What to pass as the requests library's ``verify=``.
+
+    A CA file means validate against it. SSL configured without one means
+    verification off -- self-signed certificates in development -- and that is
+    worth saying out loud, once. No SSL means the system CA bundle.
+
+    The warning is once per process: the message is about how this process was
+    configured, and a second engine repeating it says nothing new.
+    """
+    global _ssl_verify_warned
+    if cfg.ssl_ca_certs:
+        return cfg.ssl_ca_certs
+    if cfg.ssl_certfile:
+        if not _ssl_verify_warned:
+            logger.warning(
+                "SSL is enabled but --ssl-ca-certs was not provided. Certificate "
+                "verification is DISABLED for internal health checks. For "
+                "production deployments, provide --ssl-ca-certs or use CA-signed "
+                "certificates."
+            )
+            _ssl_verify_warned = True
+        return False
+    return True
diff --git a/python/sglang/srt/entrypoints/http_server.py b/python/sglang/srt/entrypoints/http_server.py
index fa76cbe12..872d1b169 100644
--- a/python/sglang/srt/entrypoints/http_server.py
+++ b/python/sglang/srt/entrypoints/http_server.py
@@ -483,6 +483,7 @@ from sglang.srt.entrypoints.v1_loads import router as v1_loads_router
 v1_loads_router.route_class = ORJSONRoute
 app.include_router(v1_loads_router)
 
+from sglang.srt.arg_groups.serving_hook import ssl_verify_of
 from sglang.srt.entrypoints.elastic_ep import router as elastic_ep_router
 from sglang.srt.runtime_context import (
     describe_kv_events_publisher,
@@ -2199,7 +2200,7 @@ def _execute_server_warmup(server_args: ServerArgs):
     if server_args.api_key:
         headers["Authorization"] = f"Bearer {server_args.api_key}"
 
-    ssl_verify = server_args.ssl_verify()
+    ssl_verify = ssl_verify_of(server_args)
 
     # Wait until the server is launched
     success = False
@@ -2375,7 +2376,7 @@ def _freeze_gc_after_server_warmup(server_args: ServerArgs):
             server_args.url() + "/freeze_gc",
             headers=freeze_headers,
             timeout=10,
-            verify=server_args.ssl_verify(),
+            verify=ssl_verify_of(server_args),
         )
         res.raise_for_status()
     except requests.exceptions.RequestException:
diff --git a/python/sglang/srt/entrypoints/http_server_engine.py b/python/sglang/srt/entrypoints/http_server_engine.py
index 063d11a6b..dee14502d 100644
--- a/python/sglang/srt/entrypoints/http_server_engine.py
+++ b/python/sglang/srt/entrypoints/http_server_engine.py
@@ -5,6 +5,7 @@ from typing import List, Optional, Tuple
 import requests
 import torch
 
+from sglang.srt.arg_groups.serving_hook import ssl_verify_of
 from sglang.srt.entrypoints.EngineBase import EngineBase
 from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import ServerArgs
@@ -24,7 +25,7 @@ def launch_server_process(server_args: ServerArgs) -> multiprocessing.Process:
     timeout = 300.0  # Increased timeout to 5 minutes for downloading large models
     start_time = time.perf_counter()
 
-    ssl_verify = server_args.ssl_verify()
+    ssl_verify = ssl_verify_of(server_args)
 
     with requests.Session() as session:
         while time.perf_counter() - start_time < timeout:
@@ -74,7 +75,7 @@ class HttpServerEngineAdapter(EngineBase):
         """
         url = f"{self.server_args.url()}/{endpoint}"
         response = requests.post(
-            url, json=payload or {}, verify=self.server_args.ssl_verify()
+            url, json=payload or {}, verify=ssl_verify_of(self.server_args)
         )
         response.raise_for_status()
         return response.json()
diff --git a/python/sglang/srt/model_executor/model_runner_components/load_model_utils.py b/python/sglang/srt/model_executor/model_runner_components/load_model_utils.py
index a115b06a0..6f22381bf 100644
--- a/python/sglang/srt/model_executor/model_runner_components/load_model_utils.py
+++ b/python/sglang/srt/model_executor/model_runner_components/load_model_utils.py
@@ -11,6 +11,10 @@ import msgspec
 import torch
 import torch.distributed as dist
 
+from sglang.srt.arg_groups.overrides import (
+    modelexpress_transport_of,
+    modelexpress_url_of,
+)
 from sglang.srt.configs.device_config import DeviceConfig
 from sglang.srt.configs.load_config import LoadConfig, LoadFormat
 from sglang.srt.constants import GPU_MEMORY_TYPE_WEIGHTS
@@ -219,8 +223,8 @@ def build_load_config(
         remote_instance_weight_loader_backend=get_model().remote_instance_weight_loader_backend,
         remote_instance_weight_loader_transfer_engine=remote_instance_weight_transporter_engine,
         remote_instance_weight_loader_transfer_engine_session_id=remote_instance_weight_transporter_session_id,
-        modelexpress_url=server_args.modelexpress_url,
-        modelexpress_transport=server_args.modelexpress_transport,
+        modelexpress_url=modelexpress_url_of(server_args),
+        modelexpress_transport=modelexpress_transport_of(server_args),
         modelopt_config=modelopt_config,
         rl_quant_profile=get_model().rl_quant_profile,
         draft_model_idx=draft_model_idx,
diff --git a/python/sglang/srt/runtime_context.py b/python/sglang/srt/runtime_context.py
index 8979e043d..898a39ac2 100644
--- a/python/sglang/srt/runtime_context.py
+++ b/python/sglang/srt/runtime_context.py
@@ -1717,8 +1717,8 @@ def pre_capture_activation_reserve_mb(gpu_mem: float | None) -> float:
 
     Derived from published leaves across four bags (``disagg`` / ``schedule`` /
     ``exec.graph`` / ``spec``) plus the configured parallel sizes, so it follows
-    a post-publish override; ``ServerArgs.pre_capture_activation_reserve_mb`` is
-    the pre-publish equivalent and
+    a post-publish override; ``pre_capture_activation_reserve_mb_of`` in
+    ``arg_groups.overrides`` is the config-shaped equivalent and
     ``TestDerivedPredicatesAgreeAcrossTiers`` pins the two equal.
     """
     schedule = get_schedule()
@@ -2100,7 +2100,7 @@ def describe_kv_events_publisher(server_args: Any) -> Optional[dict]:
     helpers the scheduler binds through — so the advertisement cannot
     drift from the sockets.
     """
-    from sglang.srt.arg_groups.overrides import resolving_view
+    from sglang.srt.arg_groups.overrides import kv_event_block_size_of, resolving_view
 
     # Lazy import so loading server_args doesn't pull in
     # disaggregation / msgspec / zmq at module top level.
@@ -2135,7 +2135,7 @@ def describe_kv_events_publisher(server_args: Any) -> Optional[dict]:
         "endpoint_host": host,
         "endpoint_port_base": port,
         "topic": cfg.topic,
-        "block_size": resolved.kv_event_block_size,
+        "block_size": kv_event_block_size_of(resolved),
         "dp_size": resolved.dp_size,
     }
     # Load range, from the same resolver SchedulerLoadPublisher binds
diff --git a/python/sglang/srt/server_args.py b/python/sglang/srt/server_args.py
index 7fa605f24..b83057c04 100644
--- a/python/sglang/srt/server_args.py
+++ b/python/sglang/srt/server_args.py
@@ -58,7 +58,6 @@ from sglang.srt.arg_groups.overrides import (
     remote_instance_transfer_engine_of,
     resolution_projection,
     resolving_view,
-    supports_mamba_cache_extra_buffer,
 )
 from sglang.srt.environ import envs
 from sglang.srt.function_call.function_call_parser import FunctionCallParser
@@ -3759,33 +3758,7 @@ class ServerArgs:
     # CUDA graph configuration resolution
     # ------------------------------------------------------------------
 
-    def pre_capture_activation_reserve_mb(self, gpu_mem: Optional[float]) -> float:
-        # Runtime activation working-set reserve for eager decode above the captured
-        # max_bs and transient prefill/logits; also covers fixed state caches.
-        cfg = resolving_view(self)
-        if cfg.disaggregation_mode == "decode":
-            running_requests = (
-                cfg.max_running_requests or cfg.cuda_graph_config.decode.max_bs or 1
-            )
-            activation_tokens = max(
-                running_requests * (cfg.speculative_num_draft_tokens or 1), 2048
-            )
-        elif cfg.chunked_prefill_size > 0:
-            activation_tokens = max(cfg.chunked_prefill_size, 2048)
-        else:
-            activation_tokens = max(cfg.max_prefill_tokens, 2048)
-        reserved_mem = (
-            512 + activation_tokens * 1.5 + cfg.tp_size * cfg.pp_size / 8 * 1024
-        )
-        if gpu_mem is not None and gpu_mem > 60 * 1024:
-            reserved_mem = max(reserved_mem, 10 * 1024)
-        return reserved_mem
-
-    def _support_mamba_cache_extra_buffer(self, model_arch: str):
-
-        return supports_mamba_cache_extra_buffer(self, model_arch)
-
-        # ===== END TO BE REFACTORED ====
+    # ===== END TO BE REFACTORED ====
 
     LANGUAGE_MODEL_ONLY_ARCHITECTURES = ("MuseGlimmerForConditionalGeneration",)
 
@@ -4137,32 +4110,6 @@ class ServerArgs:
 
         return cfg.startup_weight_load_mode == "overlap"
 
-    def ssl_verify(self):
-        """Return the value for the requests library's verify= parameter.
-
-        When SSL is configured:
-          - If a CA certificate file is provided, return its path so requests
-            validates the server certificate against that CA.
-          - Otherwise, return False to disable certificate verification
-            (suitable for self-signed certificates in development/testing).
-            A warning is logged once when this happens.
-        When SSL is not configured, return True to use the system's default
-        CA bundle.
-        """
-        if self.ssl_ca_certs:
-            return self.ssl_ca_certs
-        if self.ssl_certfile:
-            if not getattr(self, "_ssl_verify_warned", False):
-                logger.warning(
-                    "SSL is enabled but --ssl-ca-certs was not provided. "
-                    "Certificate verification is DISABLED for internal "
-                    "health checks. For production deployments, provide "
-                    "--ssl-ca-certs or use CA-signed certificates."
-                )
-                self._ssl_verify_warned = True
-            return False
-        return True
-
     def __setattr__(self, name, value):
         # Once resolution has finished the record is the READ-ONLY raw input
         # the config bags were projected from. Resolved config changes go to the bags via
@@ -4190,42 +4137,11 @@ class ServerArgs:
 
         check_server_args(self)
 
-    @property
-    def _parsed_modelexpress_config(self) -> dict:
-        cache = getattr(self, "_mx_config_cache", None)
-        if cache is not None:
-            return cache
-        if self.modelexpress_config is None:
-            result = {}
-        elif isinstance(self.modelexpress_config, str):
-            result = json.loads(self.modelexpress_config)
-        else:
-            result = self.modelexpress_config
-        self._mx_config_cache = result
-        return result
-
-    @property
-    def modelexpress_url(self) -> Optional[str]:
-        return self._parsed_modelexpress_config.get("url")
-
-    @property
-    def modelexpress_transport(self) -> str:
-        """Transport backend for modelexpress."""
-        return self._parsed_modelexpress_config.get("transport", "nixl")
-
     def remote_instance_weight_loader_use_transfer_engine(self, load_format=None):
         """``load_format`` overrides the seed's: a draft runner loading under
         ``--speculative-draft-load-format`` needs its own transfer engine."""
         return remote_instance_transfer_engine_of(resolving_view(self), load_format)
 
-    @property
-    def kv_event_block_size(self) -> int:
-        """Width KV events are emitted at: under DCP the radix tree pages at
-        ``page_size * dcp_size`` (``mem_cache/kv_cache_builder.py``).
-        """
-        cfg = resolving_view(self)
-        return cfg.page_size * self.dcp_size
-
 
 # --------------------------------------------------------------------------
 # Module-level ServerArgs helpers and runtime shims.
diff --git a/test/registered/unit/managers/test_mm_process_config.py b/test/registered/unit/managers/test_mm_process_config.py
index cf28ab1b1..c30126232 100644
--- a/test/registered/unit/managers/test_mm_process_config.py
+++ b/test/registered/unit/managers/test_mm_process_config.py
@@ -100,17 +100,22 @@ class TestBaseProcessorConfigExtraction(CustomTestCase):
         override.install()
         self.addCleanup(override.restore)
 
-        server_args = MagicMock()
-        server_args.mm_processor_worker_num = mm_processor_worker_num
-        server_args.mm_io_worker_num = mm_io_worker_num
-        server_args.mm_preprocess_cache_size_mb = None
-        server_args.tokenizer_worker_num = 1
-        server_args.trust_mm_content_hashes = False
-        server_args.media_url_max_file_size_mb = 64
-        # A bare MagicMock makes every attribute truthy, which silently sends
-        # the worker-count decision down the CPU branch. Pin what it reads.
-        server_args.disable_fast_image_processor = False
-        server_args.rl_on_policy_target = None
+        # A real record: a bare MagicMock makes every attribute truthy, which
+        # sends the worker-count decision down the wrong branch.
+        from sglang.srt.server_args import ServerArgs
+
+        server_args = ServerArgs(
+            model_path="dummy",
+            mm_process_config=mm_process_config,
+            allowed_media_domains=[],
+            mm_processor_worker_num=mm_processor_worker_num,
+            mm_io_worker_num=mm_io_worker_num,
+            mm_preprocess_cache_size_mb=None,
+            tokenizer_worker_num=1,
+            trust_mm_content_hashes=False,
+            media_url_max_file_size_mb=64,
+            disable_fast_image_processor=False,
+        )
 
         hf_config = MagicMock()
         mock_hf_processor = MagicMock()
diff --git a/test/registered/unit/server_args/test_model_source_paths.py b/test/registered/unit/server_args/test_model_source_paths.py
new file mode 100644
index 000000000..f950de003
--- /dev/null
+++ b/test/registered/unit/server_args/test_model_source_paths.py
@@ -0,0 +1,326 @@
+"""The model-source axis, on PR CI.
+
+Four ways a model path can name something that is not a local directory, and
+until now only one of them was checked before merge:
+
+- an object-store URI (``s3://`` / ``gs://`` / ``az://``), covered by
+  ``test_model_config_cache.py`` and, end to end, by a ``nightly`` test;
+- a Hub reference to a ``.gguf`` file;
+- a ModelScope repo id;
+- a remote-connector URL, which is any other ``scheme://`` and is reached by a
+  different arm of ``ModelConfig`` than the object-store one.
+
+The last three had no registered test at all. That is how a change to
+``get_model_config()``'s cache semantics went green through PR CI and broke two
+days later in the nightly: the axis it broke was not being looked at.
+
+None of this needs a network. The GGUF arm asks one resolver for a local path,
+the ModelScope arm returns any path that already exists on disk untouched and
+otherwise goes through two imports that can be stood in for, and the
+remote-connector arm goes through one factory. Each case stubs exactly that
+seam and checks what the handler declares -- and, where the path moves, that
+the model-configuration cache notices.
+"""
+
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=10, suite="base-a-test-cpu")
+
+import json
+import os
+import shutil
+import sys
+import tempfile
+import types
+import unittest
+from unittest import mock
+
+import sglang.srt.connector as connector_module
+from sglang.srt.arg_groups.model_path_hook import (
+    handle_modelscope_paths,
+    resolve_hf_gguf_model_path,
+)
+from sglang.srt.arg_groups.overrides import model_config_of, resolving_view
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.server_args import ServerArgs
+from sglang.test.test_utils import CustomTestCase
+
+_MINI_CONFIG = {
+    "architectures": ["LlamaForCausalLM"],
+    "model_type": "llama",
+    "hidden_size": 16,
+    "intermediate_size": 32,
+    "num_attention_heads": 2,
+    "num_key_value_heads": 2,
+    "num_hidden_layers": 2,
+    "vocab_size": 128,
+    "max_position_embeddings": 2048,
+}
+
+_GGUF_REFERENCE = "owner/repo"
+_MODELSCOPE_REPO = "org/model"
+_REMOTE_URL = "redis://host:6379/mini-llama"
+
+
+class _ModelSourceCase(CustomTestCase):
+    def _directory(self) -> str:
+        directory = tempfile.mkdtemp(prefix="model_source_")
+        self.addCleanup(shutil.rmtree, directory, ignore_errors=True)
+        return directory
+
+    def _checkpoint(self) -> str:
+        directory = self._directory()
+        with open(os.path.join(directory, "config.json"), "w") as handle:
+            json.dump(_MINI_CONFIG, handle)
+        return directory
+
+    def _gguf_file(self) -> str:
+        path = os.path.join(self._directory(), "model.gguf")
+        open(path, "w").close()
+        return path
+
+
+class TestTheGgufArm(_ModelSourceCase):
+    """`resolve_hf_gguf_model_path` turns a Hub reference into a local file."""
+
+    def _resolving_to(self, resolved):
+        """Stand in for the one Hub call, keyed on what it is asked about."""
+        table = resolved if isinstance(resolved, dict) else None
+
+        def _resolve(model, revision=None):
+            if table is not None:
+                return table.get(model)
+            return resolved
+
+        return mock.patch(
+            "sglang.srt.utils.hf_transformers_utils.resolve_hf_gguf_reference",
+            side_effect=_resolve,
+        )
+
+    def test_a_hub_reference_declares_the_local_path(self):
+        local = self._gguf_file()
+        server_args = ServerArgs(model_path=_GGUF_REFERENCE, device="cuda")
+        with self._resolving_to(local):
+            resolve_hf_gguf_model_path(server_args)
+
+        self.assertEqual(resolving_view(server_args).model_path, local)
+        # The record itself still carries what the operator typed.
+        self.assertEqual(server_args.model_path, _GGUF_REFERENCE)
+
+    def test_the_tokenizer_follows_only_when_it_was_the_same_reference(self):
+        local = self._gguf_file()
+        together = ServerArgs(
+            model_path=_GGUF_REFERENCE, tokenizer_path=_GGUF_REFERENCE, device="cuda"
+        )
+        with self._resolving_to(local):
+            resolve_hf_gguf_model_path(together)
+        self.assertEqual(resolving_view(together).tokenizer_path, local)
+
+        apart = ServerArgs(
+            model_path=_GGUF_REFERENCE, tokenizer_path="somewhere/else", device="cuda"
+        )
+        with self._resolving_to({_GGUF_REFERENCE: local}):
+            resolve_hf_gguf_model_path(apart)
+        self.assertEqual(resolving_view(apart).tokenizer_path, "somewhere/else")
+
+    def test_a_draft_gguf_is_resolved_on_its_own(self):
+        target, draft = self._gguf_file(), self._gguf_file()
+        server_args = ServerArgs(
+            model_path=_GGUF_REFERENCE,
+            speculative_draft_model_path="owner/draft",
+            device="cuda",
+        )
+        with self._resolving_to({_GGUF_REFERENCE: target, "owner/draft": draft}):
+            resolve_hf_gguf_model_path(server_args)
+
+        view = resolving_view(server_args)
+        self.assertEqual(view.model_path, target)
+        self.assertEqual(view.speculative_draft_model_path, draft)
+
+    def test_a_reference_that_is_not_a_gguf_declares_nothing(self):
+        server_args = ServerArgs(model_path=_GGUF_REFERENCE, device="cuda")
+        with self._resolving_to(None):
+            resolve_hf_gguf_model_path(server_args)
+        self.assertEqual(resolving_view(server_args).model_path, _GGUF_REFERENCE)
+
+    def test_the_declared_path_invalidates_the_model_configuration(self):
+        """The point of pinning the declaration: a configuration built before
+        it describes the Hub reference, not the file that was downloaded."""
+        first, second = self._checkpoint(), self._checkpoint()
+        server_args = ServerArgs(model_path=first, device="cuda")
+        before = model_config_of(server_args)
+        self.assertEqual(before.model_path, first)
+
+        with self._resolving_to(second):
+            resolve_hf_gguf_model_path(server_args)
+
+        after = model_config_of(server_args)
+        self.assertIsNot(after, before)
+        self.assertEqual(after.model_path, second)
+
+
+class TestTheModelScopeArm(_ModelSourceCase):
+    """`handle_modelscope_paths` resolves repo ids against the local cache."""
+
+    def _modelscope(self, cache_root: str, downloads: dict):
+        """Stand in for the two modules the handler imports on a cache miss."""
+        calls = []
+
+        def _snapshot_download(path, cache_dir=None, revision=None, **kwargs):
+            calls.append((path, cache_dir, revision, kwargs.get("ignore_patterns")))
+            return downloads[path]
+
+        hub = types.ModuleType("modelscope.hub.snapshot_download")
+        hub.snapshot_download = _snapshot_download
+        file_utils = types.ModuleType("modelscope.utils.file_utils")
+        file_utils.get_model_cache_root = lambda: cache_root
+        modules = {
+            "modelscope": types.ModuleType("modelscope"),
+            "modelscope.hub": types.ModuleType("modelscope.hub"),
+            "modelscope.hub.snapshot_download": hub,
+            "modelscope.utils": types.ModuleType("modelscope.utils"),
+            "modelscope.utils.file_utils": file_utils,
+        }
+        return mock.patch.dict(sys.modules, modules), calls
+
+    def test_a_path_already_on_disk_is_left_alone(self):
+        """And nothing is imported to decide that -- the arm has to stay usable
+        on a host with no modelscope installed."""
+        local = self._directory()
+        server_args = ServerArgs(model_path=local, tokenizer_path=local, device="cuda")
+        imported = {name for name in sys.modules if name.startswith("modelscope")}
+
+        handle_modelscope_paths(server_args)
+
+        view = resolving_view(server_args)
+        self.assertEqual(view.model_path, local)
+        self.assertEqual(view.tokenizer_path, local)
+        self.assertEqual(
+            imported, {name for name in sys.modules if name.startswith("modelscope")}
+        )
+
+    def test_a_repo_id_resolves_against_the_modelscope_cache(self):
+        cache_root = self._directory()
+        os.makedirs(os.path.join(cache_root, _MODELSCOPE_REPO))
+        patch, _ = self._modelscope(cache_root, {})
+        server_args = ServerArgs(
+            model_path=_MODELSCOPE_REPO, tokenizer_path=_MODELSCOPE_REPO, device="cuda"
+        )
+        with patch:
+            handle_modelscope_paths(server_args)
+
+        cached = os.path.join(cache_root, _MODELSCOPE_REPO)
+        view = resolving_view(server_args)
+        self.assertEqual(view.model_path, cached)
+        self.assertEqual(view.tokenizer_path, cached)
+
+    def test_a_cache_miss_downloads_and_the_tokenizer_skips_the_weights(self):
+        downloaded = self._directory()
+        patch, calls = self._modelscope(
+            self._directory(), {_MODELSCOPE_REPO: downloaded}
+        )
+        server_args = ServerArgs(
+            model_path=_MODELSCOPE_REPO, tokenizer_path=_MODELSCOPE_REPO, device="cuda"
+        )
+        with patch:
+            handle_modelscope_paths(server_args)
+
+        view = resolving_view(server_args)
+        self.assertEqual(view.model_path, downloaded)
+        self.assertEqual(view.tokenizer_path, downloaded)
+        # The tokenizer download does not drag the weights along with it.
+        self.assertEqual(
+            [call[3] for call in calls], [None, ["*.bin", "*.safetensors"]]
+        )
+
+    def test_the_download_directory_is_searched_before_the_hub(self):
+        download_dir = self._directory()
+        os.makedirs(os.path.join(download_dir, _MODELSCOPE_REPO))
+        patch, calls = self._modelscope(self._directory(), {})
+        server_args = ServerArgs(
+            model_path=_MODELSCOPE_REPO,
+            tokenizer_path=_MODELSCOPE_REPO,
+            download_dir=download_dir,
+            device="cuda",
+        )
+        with patch:
+            handle_modelscope_paths(server_args)
+
+        self.assertEqual(
+            resolving_view(server_args).model_path,
+            os.path.join(download_dir, _MODELSCOPE_REPO),
+        )
+        self.assertEqual(calls, [])
+
+    def test_a_draft_repo_id_is_resolved_with_its_own_revision(self):
+        cache_root = self._directory()
+        drafted = self._directory()
+        patch, calls = self._modelscope(cache_root, {"org/draft": drafted})
+        local = self._directory()
+        server_args = ServerArgs(
+            model_path=local,
+            tokenizer_path=local,
+            speculative_draft_model_path="org/draft",
+            speculative_draft_model_revision="v2",
+            device="cuda",
+        )
+        with patch:
+            handle_modelscope_paths(server_args)
+
+        self.assertEqual(
+            resolving_view(server_args).speculative_draft_model_path, drafted
+        )
+        self.assertEqual([call[2] for call in calls], ["v2"])
+
+
+class TestTheRemoteConnectorArm(_ModelSourceCase):
+    """`ModelConfig` repoints itself for any other ``scheme://``.
+
+    `redis://` is the shape the object-store arm does not claim, so it is the
+    one that reaches `_maybe_pull_model_tokenizer_from_remote`.
+    """
+
+    def _connected_to(self, directory):
+        state = {}
+
+        class _Client:
+            def pull_files(self, allow_pattern=None):
+                state["allow_pattern"] = allow_pattern
+
+            def get_local_dir(self):
+                return directory
+
+        return (
+            mock.patch.object(
+                connector_module, "create_remote_connector", return_value=_Client()
+            ),
+            state,
+        )
+
+    def test_the_configuration_reads_from_the_pulled_directory(self):
+        pulled = self._checkpoint()
+        patch, state = self._connected_to(pulled)
+        with patch:
+            config = ModelConfig(model_path=_REMOTE_URL)
+
+        self.assertEqual(config.model_path, pulled)
+        # The weights stay where they are; only the metadata was pulled.
+        self.assertEqual(config.model_weights, _REMOTE_URL)
+        self.assertEqual(state["allow_pattern"], ["*config.json"])
+
+    def test_the_record_keeps_the_url_and_the_cache_stays_keyed_on_it(self):
+        """Same movement the object-store arm makes: the configuration's path
+        moves, the record's does not, and the cache key follows the record."""
+        pulled = self._checkpoint()
+        patch, _ = self._connected_to(pulled)
+        server_args = ServerArgs(model_path=_REMOTE_URL, device="cuda")
+        with patch:
+            config = model_config_of(server_args)
+
+        self.assertEqual(server_args.model_path, _REMOTE_URL)
+        self.assertEqual(config.model_path, pulled)
+        self.assertIs(model_config_of(server_args), config)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/registered/unit/server_args/test_no_public_non_field_slot.py b/test/registered/unit/server_args/test_no_public_non_field_slot.py
index 6b2d44094..5fd2ca6da 100644
--- a/test/registered/unit/server_args/test_no_public_non_field_slot.py
+++ b/test/registered/unit/server_args/test_no_public_non_field_slot.py
@@ -59,11 +59,14 @@ def _self_written_attributes() -> set:
 class TestNoPublicNonFieldSlot(CustomTestCase):
     def test_every_public_attribute_is_a_field(self):
         written = _self_written_attributes()
-        self.assertGreater(
-            len(written),
-            3,
-            f"only {len(written)} self-writes found; the scan is broken, not the "
-            "record",
+        # Anchor on a name, not a count: the count falls every time a derived
+        # read leaves the record, so a floor erodes with what it measures.
+        self.assertIn(
+            "_resolution_finished",
+            written,
+            f"the scan did not find the resolution flag the record sets on "
+            f"itself, so it is the scan that is broken, not the record: "
+            f"{sorted(written)}",
         )
         fields = {field.name for field in dataclasses.fields(ServerArgs)}
         stray = sorted(
diff --git a/test/registered/unit/server_args/test_record_member_calls_resolve.py b/test/registered/unit/server_args/test_record_member_calls_resolve.py
new file mode 100644
index 000000000..6f92ff217
--- /dev/null
+++ b/test/registered/unit/server_args/test_record_member_calls_resolve.py
@@ -0,0 +1,144 @@
+"""Every `server_args.<name>()` in the tree names something the record has.
+
+Removing a member from `ServerArgs` means rewriting its callers, and the ones
+inside `server_args.py` are the ones you fix by reflex. The cross-file caller is
+what bites: `ServerArgs.ssl_verify()` moved to `serving_hook.ssl_verify_of()` and
+one call site kept the old spelling as `self.server_args.ssl_verify()` -- a grep
+for `server_args.ssl_verify()` does not find that, and nothing else looks. Every
+`HttpServerEngineAdapter` request raised `AttributeError` before sending.
+
+So this resolves the call sites instead of grepping for them: every attribute
+*called* on something statically known to be a record has to exist on the record.
+It is deliberately not limited to methods the refactor touched -- the next
+removal gets the same check for free.
+
+`multimodal_gen` carries a different, same-named class outside this contract, as
+the other record ratchets also record.
+"""
+
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=6, suite="base-a-test-cpu")
+
+import ast
+import dataclasses
+import pathlib
+import unittest
+
+import sglang
+from sglang.srt.server_args import ServerArgs
+from sglang.test.test_utils import CustomTestCase
+
+_ROOTS = (
+    pathlib.Path(next(iter(sglang.__path__))) / "srt",
+    pathlib.Path(__file__).resolve().parents[3],  # test/
+)
+_EXCLUDED = ("multimodal_gen",)
+
+# Attribute names that hold a `ServerArgs`. `resolving_view` and `resolved_view`
+# proxy the record but answer for names it does not carry, so they are not here.
+_RECORD_NAMES = ("server_args", "_server_args")
+
+
+def _is_record(node) -> bool:
+    """`server_args`, `self.server_args`, `self._server_args`, `cls.server_args`."""
+    if isinstance(node, ast.Name):
+        return node.id in _RECORD_NAMES
+    if isinstance(node, ast.Attribute):
+        return node.attr in _RECORD_NAMES
+    return False
+
+
+def _rebound_locally(tree) -> set:
+    """Names assigned something that is plainly not a record.
+
+    `server_args` is also a natural name for a dict of CLI flags or a list of
+    argv strings in test helpers, and those legitimately answer `.update()` and
+    `.items()`. A function that assigns one of those to the name is not talking
+    about the record in that scope.
+    """
+    literal = (ast.Dict, ast.List, ast.DictComp, ast.ListComp)
+    builders = {"dict", "list", "tuple", "set"}
+
+    def _not_a_record(value) -> bool:
+        if isinstance(value, literal):
+            return True
+        # `dict(...)` / `list(...)`, and an annotated `server_args: list[str] = [...]`
+        return (
+            isinstance(value, ast.Call) and getattr(value.func, "id", None) in builders
+        )
+
+    rebound = set()
+    for node in ast.walk(tree):
+        if isinstance(node, ast.AnnAssign):
+            targets, value = [node.target], node.value
+        elif isinstance(node, ast.Assign):
+            targets, value = node.targets, node.value
+        else:
+            continue
+        if value is None or not _not_a_record(value):
+            continue
+        for target in targets:
+            if isinstance(target, ast.Name) and target.id in _RECORD_NAMES:
+                rebound.add(target.id)
+    return rebound
+
+
+def _called_members():
+    """{name: [file:line]} for every `<record>.<name>(...)` in the tree."""
+    found: dict[str, list[str]] = {}
+    for root in _ROOTS:
+        for path in sorted(root.rglob("*.py")):
+            text = path.as_posix()
+            if any(part in text for part in _EXCLUDED):
+                continue
+            source = path.read_text(encoding="utf-8-sig")
+            if "server_args" not in source:
+                continue
+            try:
+                tree = ast.parse(source)
+            except SyntaxError:
+                continue
+            rebound = _rebound_locally(tree)
+            for node in ast.walk(tree):
+                if (
+                    isinstance(node, ast.Call)
+                    and isinstance(node.func, ast.Attribute)
+                    and _is_record(node.func.value)
+                    and getattr(node.func.value, "id", None) not in rebound
+                ):
+                    found.setdefault(node.func.attr, []).append(
+                        f"{path.name}:{node.lineno}"
+                    )
+    return found
+
+
+class TestRecordMemberCallsResolve(CustomTestCase):
+    def test_every_called_member_exists_on_the_record(self):
+        called = _called_members()
+        self.assertGreater(
+            len(called),
+            5,
+            f"only {len(called)} members called on a record; the scan is broken, "
+            "not the tree",
+        )
+        available = set(dir(ServerArgs)) | {
+            field.name for field in dataclasses.fields(ServerArgs)
+        }
+        missing = {
+            name: sites
+            for name, sites in sorted(called.items())
+            if name not in available
+        }
+        self.assertEqual(
+            {},
+            missing,
+            "these are called on a ServerArgs but the record has no such member -- "
+            "each one raises AttributeError at the call. A member that moved out of "
+            "the record has to be rewritten at every call site, including the ones "
+            f"reached through `self.server_args`: {missing}",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/registered/unit/server_args/test_server_args.py b/test/registered/unit/server_args/test_server_args.py
index 45384d6ff..30361e6c5 100644
--- a/test/registered/unit/server_args/test_server_args.py
+++ b/test/registered/unit/server_args/test_server_args.py
@@ -55,6 +55,7 @@ from sglang.srt.arg_groups.serving_hook import (
     handle_multimodal_feature_transport,
     handle_ssl_validation,
     handle_tokenizer_batching,
+    ssl_verify_of,
 )
 from sglang.srt.arg_groups.speculative_hook import handle_speculative_decoding
 from sglang.srt.arg_groups.validation_hook import check_two_batch_overlap
@@ -1367,13 +1368,15 @@ class TestSSLArgs(unittest.TestCase):
         self.assertTrue(server_args.url().startswith("https://"))
 
     def test_ssl_verify_without_ssl(self):
+        # the derived read lives with the rest of the SSL handling now
+
         server_args = ServerArgs(model_path="dummy")
-        self.assertIs(server_args.ssl_verify(), True)
+        self.assertIs(ssl_verify_of(server_args), True)
 
     @patch("os.path.isfile", return_value=True)
     def test_ssl_verify_with_ssl_no_ca(self, _mock_isfile):
         server_args = self._validate_ssl(ssl_keyfile="key.pem", ssl_certfile="cert.pem")
-        self.assertIs(server_args.ssl_verify(), False)
+        self.assertIs(ssl_verify_of(server_args), False)
 
     @patch("os.path.isfile", return_value=True)
     def test_ssl_verify_with_ssl_and_ca(self, _mock_isfile):
@@ -1382,7 +1385,7 @@ class TestSSLArgs(unittest.TestCase):
             ssl_certfile="cert.pem",
             ssl_ca_certs="ca.pem",
         )
-        self.assertEqual(server_args.ssl_verify(), "ca.pem")
+        self.assertEqual(ssl_verify_of(server_args), "ca.pem")
 
     def test_ssl_ca_certs_without_certfile_raises(self):
         with self.assertRaises(ValueError) as context:
@@ -2840,8 +2843,13 @@ class TestDcpKvEventContract(CustomTestCase):
     def test_kv_event_block_size_widens_a_single_token_page(self):
         # page_size=1 + DCP is a real deployment shape: the allocator is still
         # paged, at dcp_size.
+        from sglang.srt.arg_groups.overrides import (
+            kv_event_block_size_of,
+            resolving_view,
+        )
+
         args = ServerArgs(model_path="dummy", tp_size=8, dcp_size=8, page_size=1)
-        self.assertEqual(args.kv_event_block_size, 8)
+        self.assertEqual(kv_event_block_size_of(resolving_view(args)), 8)
 
 
 if __name__ == "__main__":
diff --git a/test/registered/unit/test_chain_read_ratchet.py b/test/registered/unit/test_chain_read_ratchet.py
index 1841cc222..99abbcdee 100644
--- a/test/registered/unit/test_chain_read_ratchet.py
+++ b/test/registered/unit/test_chain_read_ratchet.py
@@ -125,19 +125,17 @@ def _returned_field_names(function):
             and node.func.value.id in returned
         ):
             names |= {kw.arg for kw in node.keywords if kw.arg}
-            receiver = node.func.value
-            if isinstance(receiver, ast.Name) and receiver.id == "overrides":
-                # Positional dict literals are collected by the Dict walk;
-                # anything else is invisible.
-                for arg in node.args:
-                    if not isinstance(arg, ast.Dict):
-                        raise AssertionError(
-                            f"opaque overrides.update() argument in {function.name}"
-                        )
-                if any(kw.arg is None for kw in node.keywords):
-                    raise AssertionError(
-                        f"**kwargs overrides.update() in {function.name}"
-                    )
+            # A positional dict literal has to be read here. The Dict walk above
+            # only reaches literals that are *returned* or assigned to a returned
+            # name, so `d.update({"field": value})` was being type-checked and
+            # then dropped -- silently, under a comment claiming otherwise.
+            for arg in node.args:
+                if isinstance(arg, ast.Dict):
+                    top_level_keys(arg)
+                else:
+                    raise AssertionError(f"opaque update() argument in {function.name}")
+            if any(kw.arg is None for kw in node.keywords):
+                raise AssertionError(f"**kwargs update() in {function.name}")
     return names
 
 
@@ -151,12 +149,28 @@ def _declared_by_registry_and_passes():
     `@register_model_override*` sees exactly one of them and reports a healthy
     census over a channel it cannot see.
     """
+    import sys
+
     from sglang.srt.arg_groups import overrides
 
-    tree = ast.parse((_SRT / "arg_groups/overrides.py").read_text(encoding="utf-8-sig"))
-    bodies = {
-        node.name: node for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)
-    }
+    # Resolve each callable's body in the file it actually lives in. The
+    # declarations are spread over `arg_groups/model_overrides/`, one module per
+    # model family, and a scan hard-coded to `overrides.py` would find none of
+    # them -- and, worse, would keep reporting a healthy census while doing it.
+    bodies_by_module = {}
+
+    def _bodies(module_name):
+        if module_name not in bodies_by_module:
+            path = getattr(sys.modules[module_name], "__file__", None)
+            assert path, f"{module_name} has no source file"
+            module_tree = ast.parse(pathlib.Path(path).read_text(encoding="utf-8-sig"))
+            bodies_by_module[module_name] = {
+                node.name: node
+                for node in ast.walk(module_tree)
+                if isinstance(node, ast.FunctionDef)
+            }
+        return bodies_by_module[module_name]
+
     callables = {fn for fns in overrides._MODEL_OVERRIDE_FNS.values() for fn in fns}
     callables |= {
         fn for _predicate, fn in getattr(overrides, "_PREDICATE_OVERRIDE_FNS", ())
@@ -165,11 +179,23 @@ def _declared_by_registry_and_passes():
 
     fields = set()
     for fn in callables:
-        body = bodies.get(getattr(fn, "__name__", ""))
-        if body is not None:
-            fields |= _returned_field_names(body)
+        name = getattr(fn, "__name__", "")
+        body = _bodies(fn.__module__).get(name)
+        # Loud, not silent: a body this scan cannot find is a field census it
+        # is not taking, and a narrower census makes every check downstream of
+        # it quietly vacuous.
+        assert body is not None, f"{fn.__module__}.{name} has no body to scan"
+        fields |= _returned_field_names(body)
+
     # The literal arch -> {field: value} table, which has no callable at all.
-    for node in tree.body:
+    # It lives with the rest of the registry, in `model_override_base`.
+    from sglang.srt.arg_groups import model_override_base
+
+    table_tree = ast.parse(
+        pathlib.Path(model_override_base.__file__).read_text(encoding="utf-8-sig")
+    )
+    seen_table = False
+    for node in table_tree.body:
         target = None
         if isinstance(node, ast.Assign) and isinstance(node.targets[0], ast.Name):
             target = node.targets[0].id
@@ -186,6 +212,8 @@ def _declared_by_registry_and_passes():
                 if not isinstance(key, ast.Constant):
                     raise AssertionError("non-literal override key")
                 fields.add(key.value)
+        seen_table = True
+    assert seen_table, "MODEL_OVERRIDES is not where this scan looks for it"
     return fields
 
 
diff --git a/test/registered/unit/test_model_override_split.py b/test/registered/unit/test_model_override_split.py
new file mode 100644
index 000000000..b5a4858f0
--- /dev/null
+++ b/test/registered/unit/test_model_override_split.py
@@ -0,0 +1,120 @@
+"""Two family modules must never declare the same field for the same architecture.
+
+An architecture claimed by two family modules is normal -- ``Qwen3NextForCausalLM``
+gets its attention shape from ``qwen3_5`` and its MoE runner from ``qwen3_moe``.
+Two modules declaring the *same* field for it is not: nobody owns that value,
+and which module supplies it is decided by nothing more deliberate than the
+order the imports happen to be in. That is a defect in the declarations, so
+this forbids it outright rather than choosing a winner.
+
+The ordering follows from the rule and is not itself pinned. ``__init__.py`` is
+a list of imports, importing is what registers, and the gate applies matching
+declarations in registration order with the last writer winning -- so an
+overlap would make an import list into a behavioural statement, which tools
+reorder freely. With no overlap the list can be sorted however anyone likes.
+
+The declared-field sets are read with the chain ratchet's own extractor rather
+than a second implementation of the same scan, for the reason its docstring
+gives: two censuses of one thing that disagree are worse than either alone.
+"""
+
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=5, suite="base-a-test-cpu")
+
+import ast
+import importlib.util
+import pathlib
+import sys
+import unittest
+
+from sglang.srt.arg_groups import model_overrides
+from sglang.srt.arg_groups.model_override_base import (
+    _MODEL_OVERRIDE_FNS,
+    MODEL_OVERRIDES,
+)
+from sglang.test.test_utils import CustomTestCase
+
+_RATCHET = pathlib.Path(__file__).resolve().parent / "test_chain_read_ratchet.py"
+
+
+def _returned_field_names(fn):
+    spec = importlib.util.spec_from_file_location("_chain_ratchet_for_split", _RATCHET)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    source = pathlib.Path(sys.modules[fn.__module__].__file__).read_text(
+        encoding="utf-8-sig"
+    )
+    body = next(
+        node
+        for node in ast.walk(ast.parse(source))
+        if isinstance(node, ast.FunctionDef) and node.name == fn.__name__
+    )
+    return module._returned_field_names(body)
+
+
+class TestModelOverrideSplit(CustomTestCase):
+    def test_no_field_is_declared_by_two_family_modules(self):
+        contested = {
+            arch: fns for arch, fns in _MODEL_OVERRIDE_FNS.items() if len(fns) > 1
+        }
+        self.assertTrue(contested, "the scan found no architecture with two claimants")
+        for arch, fns in sorted(contested.items()):
+            with self.subTest(architecture=arch):
+                seen: dict[str, str] = {}
+                for fn in fns:
+                    for field in _returned_field_names(fn):
+                        earlier = seen.get(field)
+                        self.assertIsNone(
+                            earlier,
+                            f"{arch}: {fn.__module__}.{fn.__name__} and {earlier} "
+                            f"both declare {field!r}, so which one wins now depends "
+                            f"on the order of the imports in "
+                            f"arg_groups/model_overrides/__init__.py",
+                        )
+                        seen[field] = f"{fn.__module__}.{fn.__name__}"
+
+    def test_the_constant_table_does_not_contest_a_callable(self):
+        """``MODEL_OVERRIDES`` applies before the callables, so a field it and a
+        callable both name is decided by that ordering instead."""
+        for arch, const in sorted(MODEL_OVERRIDES.items()):
+            for fn in _MODEL_OVERRIDE_FNS.get(arch, ()):
+                with self.subTest(architecture=arch, fn=fn.__name__):
+                    self.assertFalse(
+                        set(const) & _returned_field_names(fn),
+                        f"{arch}: MODEL_OVERRIDES and {fn.__name__} both declare "
+                        f"{sorted(set(const) & _returned_field_names(fn))}",
+                    )
+
+    def test_the_import_list_names_every_family_module(self):
+        """Importing is what registers, so a module missing from the list is a
+        family that silently stops applying -- and the tests that import a
+        provider directly would not notice."""
+        package = pathlib.Path(model_overrides.__file__).parent
+        on_disk = {
+            path.stem for path in package.glob("*.py") if path.stem != "__init__"
+        }
+        imported = {
+            alias.name
+            for node in ast.walk(ast.parse((package / "__init__.py").read_text()))
+            if isinstance(node, ast.ImportFrom)
+            and node.module == "sglang.srt.arg_groups.model_overrides"
+            for alias in node.names
+        }
+        self.assertEqual(on_disk, imported)
+
+    def test_every_declaration_comes_from_its_own_family_module(self):
+        """The split itself: nothing was left behind in overrides.py."""
+        for arch, fns in _MODEL_OVERRIDE_FNS.items():
+            for fn in fns:
+                with self.subTest(architecture=arch, fn=fn.__name__):
+                    self.assertTrue(
+                        fn.__module__.startswith(
+                            "sglang.srt.arg_groups.model_overrides."
+                        ),
+                        f"{fn.__name__} still lives in {fn.__module__}",
+                    )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/registered/unit/test_model_overrides.py b/test/registered/unit/test_model_overrides.py
index c16b7e76a..3659a2444 100644
--- a/test/registered/unit/test_model_overrides.py
+++ b/test/registered/unit/test_model_overrides.py
@@ -15,8 +15,11 @@ from types import SimpleNamespace
 from typing import Optional
 from unittest.mock import patch
 
+from sglang.srt.arg_groups import model_override_base as base_module
 from sglang.srt.arg_groups import overrides as overrides_module
 from sglang.srt.arg_groups.arg_utils import A, Arg, resolvable_fields
+from sglang.srt.arg_groups.model_overrides import minicpm as minicpm_module
+from sglang.srt.arg_groups.model_overrides import qwen3_5 as qwen3_5_module
 from sglang.srt.arg_groups.overrides import (
     collect_model_override_declarations,
     register_model_override,
@@ -119,15 +122,30 @@ class TestDSparkCheckpointConfig(CustomTestCase):
         self.assertTrue(get_dspark_sample_from_anchor(SimpleNamespace()))
 
 
+def _hf(quant_method=None, **kw):
+    """An hf config that states its own quantization.
+
+    `get_quantization_config(hf_config)` just reads
+    `hf_config.quantization_config["quant_method"]`, so a test says what the
+    checkpoint is by handing over a config that says it -- rather than stubbing
+    the reader in one module and hoping that is the module doing the reading.
+    """
+    if quant_method is not None:
+        kw["quantization_config"] = {"quant_method": quant_method}
+    return SimpleNamespace(**kw)
+
+
 class _IsolatedRegistry(CustomTestCase):
     """Run each test against empty registries (they are process-global)."""
 
     def setUp(self):
         super().setUp()
+        # The registries live in `model_override_base`; that is the one address
+        # to isolate, because the registrars and the collector both use it.
         self._patches = [
-            patch.dict(overrides_module.MODEL_OVERRIDES, clear=True),
-            patch.dict(overrides_module._MODEL_OVERRIDE_FNS, clear=True),
-            patch.object(overrides_module, "_PREDICATE_OVERRIDE_FNS", []),
+            patch.dict(base_module.MODEL_OVERRIDES, clear=True),
+            patch.dict(base_module._MODEL_OVERRIDE_FNS, clear=True),
+            patch.object(base_module, "_PREDICATE_OVERRIDE_FNS", []),
         ]
         for p in self._patches:
             p.start()
@@ -140,7 +158,7 @@ class _IsolatedRegistry(CustomTestCase):
 
 class TestModelOverrideRegistry(_IsolatedRegistry):
     def test_const_then_callables_in_registration_order(self):
-        overrides_module.MODEL_OVERRIDES["FakeForCausalLM"] = {"a": 1}
+        base_module.MODEL_OVERRIDES["FakeForCausalLM"] = {"a": 1}
 
         @register_model_override("FakeForCausalLM")
         def _first(server_args, hf_config):
@@ -453,7 +471,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         )
 
         with override_platform(is_blackwell=False):
-            overrides = overrides_module._minicpm_sala_overrides(args, config)
+            overrides = minicpm_module._minicpm_sala_overrides(args, config)
 
         self.assertTrue(overrides["disable_radix_cache"])
         self.assertEqual(overrides["attention_backend"], "minicpm_flashattn")
@@ -660,7 +678,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
     def test_mimo_v2_declarations(self):
         # Callable-level golden: MiMoV2 archs are hybrid (config-shape heavy),
         # so the declaration is pinned directly for both provider inputs.
-        from sglang.srt.arg_groups.overrides import _mimo_v2_overrides
+        from sglang.srt.arg_groups.model_overrides.mimo_v2 import _mimo_v2_overrides
 
         def _args(**kw):
             defaults = dict(speculative_algorithm=None, moe_runner_backend="auto")
@@ -677,7 +695,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
 
     def test_mimo_v2_sm100_fp8_pins_flashinfer_trtllm_moe(self):
         """Blackwell FP8 must not be left on the triton fused-MoE runner."""
-        from sglang.srt.arg_groups.overrides import _mimo_v2_overrides
+        from sglang.srt.arg_groups.model_overrides.mimo_v2 import _mimo_v2_overrides
 
         def _args(**kw):
             defaults = dict(speculative_algorithm=None, moe_runner_backend="auto")
@@ -685,23 +703,17 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             return SimpleNamespace(**defaults)
 
         with override_platform(is_sm100=True):
-            with patch.object(
-                overrides_module, "get_quantization_config", return_value="fp8"
-            ):
-                self.assertEqual(
-                    _mimo_v2_overrides(_args(), None),
-                    {"moe_runner_backend": "flashinfer_trtllm"},
-                )
-                # An explicit user choice is never overwritten.
-                self.assertEqual(
-                    _mimo_v2_overrides(_args(moe_runner_backend="triton"), None), {}
-                )
+            self.assertEqual(
+                _mimo_v2_overrides(_args(), _hf("fp8")),
+                {"moe_runner_backend": "flashinfer_trtllm"},
+            )
+            # An explicit user choice is never overwritten.
+            self.assertEqual(
+                _mimo_v2_overrides(_args(moe_runner_backend="triton"), _hf("fp8")), {}
+            )
             # FP4 checkpoints run through flashinfer_mxfp4, so they must not be
             # pinned to flashinfer_trtllm.
-            with patch.object(
-                overrides_module, "get_quantization_config", return_value="mxfp4"
-            ):
-                self.assertEqual(_mimo_v2_overrides(_args(), None), {})
+            self.assertEqual(_mimo_v2_overrides(_args(), _hf("mxfp4")), {})
 
     def test_mimo_v2_family_is_registered(self):
         with override_platform(is_sm100=False):
@@ -748,7 +760,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         )
 
     def test_nemotron_h_w4a16_moe_uses_marlin_on_sm100(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(
             quantized_layers={
@@ -778,7 +792,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             )
 
     def test_nemotron_h_nvfp4_moe_keeps_flashinfer_trtllm_on_sm100(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(
             quantized_layers={
@@ -808,7 +824,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             )
 
     def test_nemotron_h_speculation_uses_arch_specific_attention_on_blackwell(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         cases = {
             True: {
@@ -836,7 +854,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
                         self.assertEqual(overrides[key], value)
 
     def test_nemotron_h_sm100_speculative_draft_backend_matrix(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         for algorithm in ("EAGLE", "NEXTN", "DSPARK"):
             with self.subTest(algorithm=algorithm):
@@ -864,7 +884,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         self.assertNotIn("speculative_draft_attention_backend", overrides)
 
     def test_nemotron_h_sm100_speculation_preserves_explicit_cache_and_draft(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(quantized_layers={})
         server_args.speculative_algorithm = "DSPARK"
@@ -884,7 +906,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         self.assertNotIn("speculative_draft_attention_backend", overrides)
 
     def test_nemotron_h_sm100_topk_tree_falls_back_to_triton(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(quantized_layers={})
         server_args.speculative_algorithm = "EAGLE"
@@ -902,7 +926,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         self.assertNotIn("mamba_radix_cache_strategy", overrides)
 
     def test_nemotron_h_target_only_sm120_defers_to_generic_attention_default(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(quantized_layers={})
 
@@ -915,7 +941,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             )
 
     def test_nemotron_h_target_only_sm100_uses_trtllm_mha(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(quantized_layers={})
 
@@ -929,7 +957,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             )
 
     def test_nemotron_h_explicit_split_attention_backend_wins(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(quantized_layers={})
         server_args.speculative_algorithm = "DFLASH"
@@ -945,7 +975,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         self.assertNotIn("speculative_draft_attention_backend", overrides)
 
     def test_nemotron_h_w4a16_moe_rejects_a2a_backend(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(
             quantized_layers={
@@ -961,7 +993,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             _nemotron_h_overrides(server_args, hf_config)
 
     def test_nemotron_h_w4a16_moe_rejects_non_marlin_runner(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         server_args, hf_config = self._nemotron_h_args(
             quantized_layers={
@@ -1021,7 +1055,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         self.assertTrue((self._publish(sa), self._leaf("disable_hybrid_swa_memory"))[1])
 
     def test_exaone_without_pattern_declares_nothing(self):
-        from sglang.srt.arg_groups.overrides import _exaone_overrides
+        from sglang.srt.arg_groups.model_overrides.exaone import _exaone_overrides
 
         self.assertEqual(
             _exaone_overrides(None, SimpleNamespace(sliding_window_pattern=None)),
@@ -1049,7 +1083,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         self.assertEqual((self._publish(sa), self._leaf("dtype"))[1], "auto")
 
     def test_gpt_oss_xpu_dtype_validation_reads_pristine(self):
-        from sglang.srt.arg_groups.overrides import _gpt_oss_overrides
+        from sglang.srt.arg_groups.model_overrides.gpt_oss import _gpt_oss_overrides
 
         with override_platform(is_xpu=True):
             with self.assertRaises(NotImplementedError):
@@ -1427,7 +1461,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             )
 
     def test_deepseek_v4_overrides_at_callable_level(self):
-        from sglang.srt.arg_groups.overrides import _deepseek_v4_overrides
+        from sglang.srt.arg_groups.model_overrides.deepseek_v4 import (
+            _deepseek_v4_overrides,
+        )
         from sglang.srt.server_args import ServerArgs
 
         hf = SimpleNamespace(architectures=["DeepseekV4ForCausalLM"])
@@ -1540,7 +1576,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         )
 
     def test_nemotron_h_overrides_at_callable_level(self):
-        from sglang.srt.arg_groups.overrides import _nemotron_h_overrides
+        from sglang.srt.arg_groups.model_overrides.nemotron_h import (
+            _nemotron_h_overrides,
+        )
 
         def _hf(quant_algo="NVFP4", *, include_quantization_config=True):
             hf = SimpleNamespace(
@@ -1892,7 +1930,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
                 _cutedsl_prefill_backend_fill(_view())
 
     def test_moss_vl_overrides_at_callable_level(self):
-        from sglang.srt.arg_groups.overrides import _moss_vl_overrides
+        from sglang.srt.arg_groups.model_overrides.moss_vl import _moss_vl_overrides
 
         def _args(**kw):
             defaults = dict(
@@ -2183,7 +2221,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         )
 
     def test_qwen3_5_hybrid_coupled_declaration(self):
-        from sglang.srt.arg_groups.overrides import _qwen3_5_hybrid_overrides
+        from sglang.srt.arg_groups.model_overrides.qwen3_5 import (
+            _qwen3_5_hybrid_overrides,
+        )
 
         def _args(default_backend, **kw):
             defaults = dict(
@@ -2201,7 +2241,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             return args
 
         with override_platform(is_sm100=True), patch.object(
-            overrides_module,
+            qwen3_5_module,
             "get_default_attn_backend",
             lambda server_args, **_: server_args.default_backend_for_test,
         ):
@@ -2246,7 +2286,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             self.assertEqual(_qwen3_5_hybrid_overrides(_args("fa3"), None), {})
 
     def test_qwen3vl_page_size(self):
-        from sglang.srt.arg_groups.overrides import _qwen3vl_overrides
+        from sglang.srt.arg_groups.model_overrides.qwen3_vl import _qwen3vl_overrides
 
         with override_platform(is_hip=True):
             with patch("sglang.srt.environ.envs.SGLANG_USE_AITER_UNIFIED_ATTN") as e:
@@ -2319,7 +2359,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             )
 
     def test_m3_fp8_attn_gemm_resolution(self):
-        from sglang.srt.arg_groups.overrides import _minimax_m3_overrides
+        from sglang.srt.arg_groups.model_overrides.minimax_m3 import (
+            _minimax_m3_overrides,
+        )
         from sglang.srt.server_args import m3_fp8_attn_gemm_enabled
 
         def _args(**kw):
@@ -2364,9 +2406,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             return ns
 
         hf = SimpleNamespace()
-        with override_platform(is_hip=False), override_platform(
-            is_sm100=True
-        ), patch.object(overrides_module, "get_quantization_config", return_value=None):
+        # `hf` carries no `quantization_config`, which is what an unquantized
+        # checkpoint looks like -- no stub needed to say so.
+        with override_platform(is_hip=False), override_platform(is_sm100=True):
             # fp8_e4m3 KV: SM100 backend default flips to trtllm_mha (the only
             # dense backend with the fp8-q GEMM path); page snaps to 128
             ov = _minimax_m3_overrides(_m3_args(kv_cache_dtype="fp8_e4m3"), hf)
@@ -2378,7 +2420,7 @@ class TestGoldenModelOverrides(_IsolatedPublish):
             self.assertEqual(ov["page_size"], 128)
             # e5m2 KV: stays on fa4 + the widening Triton path, and warns
             with self.assertLogs(
-                "sglang.srt.arg_groups.overrides", level="WARNING"
+                "sglang.srt.arg_groups.model_overrides.minimax_m3", level="WARNING"
             ) as logs:
                 ov = _minimax_m3_overrides(_m3_args(kv_cache_dtype="fp8_e5m2"), hf)
             self.assertEqual(ov["attention_backend"], "fa4")
@@ -2505,13 +2547,17 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         )
 
     def test_monolith_attention_families_at_callable_level(self):
-        from sglang.srt.arg_groups.overrides import (
+        from sglang.srt.arg_groups.model_overrides.falcon_h1 import (
             _falcon_h1_jet_overrides,
-            _gemma4_overrides,
-            _glm4_moe_overrides,
+        )
+        from sglang.srt.arg_groups.model_overrides.gemma4 import _gemma4_overrides
+        from sglang.srt.arg_groups.model_overrides.glm4_moe import _glm4_moe_overrides
+        from sglang.srt.arg_groups.model_overrides.granitemoehybrid import (
             _granite_moe_hybrid_overrides,
-            _lfm2_overrides,
-            _llama4_overrides,
+        )
+        from sglang.srt.arg_groups.model_overrides.lfm2 import _lfm2_overrides
+        from sglang.srt.arg_groups.model_overrides.llama4 import _llama4_overrides
+        from sglang.srt.arg_groups.model_overrides.minicpmv import (
             _minicpm_v4_6_overrides,
         )
 
@@ -2673,7 +2719,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
         )
 
     def test_deepseek_family_order_safe_declarations(self):
-        from sglang.srt.arg_groups.overrides import _deepseek_family_overrides
+        from sglang.srt.arg_groups.model_overrides.deepseek_v2 import (
+            _deepseek_family_overrides,
+        )
 
         def _args(**kw):
             defaults = dict(
@@ -2768,27 +2816,26 @@ class TestGoldenModelOverrides(_IsolatedPublish):
                 self.assertEqual(_deepseek_family_overrides(_args(), None), {})
 
     def test_qwen3_moe_family_quant_absorption(self):
-        from sglang.srt.arg_groups.overrides import _qwen3_moe_family_overrides
+        from sglang.srt.arg_groups.model_overrides.qwen3_moe import (
+            _qwen3_moe_family_overrides,
+        )
 
         with override_platform(is_sm100=True):
-            with patch.object(
-                overrides_module, "get_quantization_config", return_value="fp8"
-            ):
-                self.assertEqual(
-                    _qwen3_moe_family_overrides(
-                        SimpleNamespace(
-                            quantization=None,
-                            _quantization_explicitly_unset=False,
-                            moe_a2a_backend="none",
-                            moe_runner_backend="auto",
-                        ),
-                        SimpleNamespace(architectures=["Qwen3MoeForCausalLM"]),
+            self.assertEqual(
+                _qwen3_moe_family_overrides(
+                    SimpleNamespace(
+                        quantization=None,
+                        _quantization_explicitly_unset=False,
+                        moe_a2a_backend="none",
+                        moe_runner_backend="auto",
                     ),
-                    {
-                        "quantization": "fp8",
-                        "moe_runner_backend": "flashinfer_trtllm",
-                    },
-                )
+                    _hf("fp8", architectures=["Qwen3MoeForCausalLM"]),
+                ),
+                {
+                    "quantization": "fp8",
+                    "moe_runner_backend": "flashinfer_trtllm",
+                },
+            )
         with override_platform(is_sm100=False):
             self.assertEqual(_qwen3_moe_family_overrides(None, None), {})
 
diff --git a/test/registered/unit/test_runtime_context.py b/test/registered/unit/test_runtime_context.py
index cd04d764c..aa1d9c52c 100644
--- a/test/registered/unit/test_runtime_context.py
+++ b/test/registered/unit/test_runtime_context.py
@@ -1200,6 +1200,9 @@ class TestDerivedPredicatesAgreeAcrossTiers(_IsolatedServerArgs):
     def test_activation_reserve_matches_the_member(self):
         from types import SimpleNamespace
 
+        from sglang.srt.arg_groups.overrides import (
+            pre_capture_activation_reserve_mb_of,
+        )
         from sglang.srt.runtime_context import pre_capture_activation_reserve_mb
 
         graph = SimpleNamespace(decode=SimpleNamespace(max_bs=64))
@@ -1231,7 +1234,7 @@ class TestDerivedPredicatesAgreeAcrossTiers(_IsolatedServerArgs):
                     args = _FakeResolvedArgs(cuda_graph_config=graph, **case)
                     get_context().set_server_args(args)
                     self.assertEqual(
-                        ServerArgs.pre_capture_activation_reserve_mb(args, gpu_mem),
+                        pre_capture_activation_reserve_mb_of(args, gpu_mem),
                         pre_capture_activation_reserve_mb(gpu_mem),
                     )