diff --git a/python/sglang/srt/entrypoints/engine.py b/python/sglang/srt/entrypoints/engine.py
index 5c7279b80..46cfa4dfe 100644
--- a/python/sglang/srt/entrypoints/engine.py
+++ b/python/sglang/srt/entrypoints/engine.py
@@ -28,8 +28,6 @@ import multiprocessing as mp
 import os
 import random
 import signal
-import subprocess
-import sys
 import tempfile
 import threading
 import time
@@ -672,12 +670,6 @@ class Engine(EngineScoreMixin, EngineBase):
         (``python -m sglang.srt.weight_cache.daemon``) plus
         ``--weight-cache-mode client``, where the daemon outlives the engine.
         """
-        if get_parallel().dp_size > 1:
-            raise ValueError(
-                "Weight cache daemon mode does not support dp_size > 1. "
-                "Please set --dp-size 1 when using --weight-cache-mode daemon."
-            )
-
         # Multi-node needs an explicit rendezvous address; otherwise each node
         # picks its own local 127.0.0.1 port (below) and the per-node daemons
         # can never form the joint process group.
@@ -721,6 +713,7 @@ class Engine(EngineScoreMixin, EngineBase):
 
         # Validate and clean up stale .ready/.sock files from prior runs.
         # If a daemon is still alive at this rank, raise instead of clobbering.
+        from sglang.srt.weight_cache.daemon import spawn_weight_cache_daemon
         from sglang.srt.weight_cache.protocol import (
             cleanup_stale_daemon_files,
             compute_global_rank,
@@ -743,49 +736,14 @@ class Engine(EngineScoreMixin, EngineBase):
                     base_gpu_id=server_args.base_gpu_id,
                     gpu_id_step=server_args.gpu_id_step,
                 )
-                cmd = [
-                    sys.executable,
-                    "-m",
-                    "sglang.srt.weight_cache.daemon",
-                    "--model-path",
-                    get_model().model_path,
-                    "--gpu-id",
-                    str(gpu_id),
-                    "--tp-size",
-                    str(tp_size),
-                    "--tp-rank",
-                    str(tp_rank),
-                    "--pp-size",
-                    str(configured_pp_size()),
-                    "--pp-rank",
-                    str(pp_rank),
-                    "--dp-size",
-                    "1",
-                    "--ep-size",
-                    str(get_parallel().ep_size),
-                    "--load-format",
-                    get_model().load_format,
-                    "--dtype",
-                    get_model().dtype,
-                    "--dist-init-method",
-                    dist_init_method,
-                ]
-                if get_model().quantization:
-                    cmd += ["--quantization", get_model().quantization]
-                if (
-                    server_args.model_loader_extra_config
-                    and server_args.model_loader_extra_config != "{}"
-                ):
-                    cmd += [
-                        "--model-loader-extra-config",
-                        server_args.model_loader_extra_config,
-                    ]
-                if server_args.trust_remote_code:
-                    cmd += ["--trust-remote-code"]
-                if server_args.revision:
-                    cmd += ["--revision", server_args.revision]
+                proc = spawn_weight_cache_daemon(
+                    server_args,
+                    gpu_id=gpu_id,
+                    tp_rank=tp_rank,
+                    pp_rank=pp_rank,
+                    dist_init_method=dist_init_method,
+                )
 
-                proc = subprocess.Popen(cmd)
                 daemon_procs.append(proc)
 
         # Wait for all daemons to be ready (ready file exists). On any failure
@@ -810,10 +768,10 @@ class Engine(EngineScoreMixin, EngineBase):
                             )
                         # Check if daemon process is still alive
                         for p in daemon_procs:
-                            if p.poll() is not None:
+                            if not p.is_alive():
                                 raise RuntimeError(
                                     f"Weight cache daemon (pid={p.pid}) exited prematurely "
-                                    f"with code {p.returncode}"
+                                    f"with code {p.exitcode}"
                                 )
                     logger.info(
                         f"Weight cache daemon for pp_rank={pp_rank} "
@@ -844,17 +802,17 @@ class Engine(EngineScoreMixin, EngineBase):
         if not procs:
             return
         for p in procs:
-            if p.poll() is None:
+            if p.is_alive():
                 p.terminate()  # SIGTERM -> daemon cleanup handler runs
         for p in procs:
-            try:
-                p.wait(timeout=timeout)
-            except subprocess.TimeoutExpired:
+            p.join(timeout=timeout)
+            if p.is_alive():
                 logger.warning(
                     f"Weight cache daemon (pid={p.pid}) did not exit within "
                     f"{timeout}s of SIGTERM; sending SIGKILL."
                 )
                 p.kill()
+                p.join()
 
     @classmethod
     def _launch_scheduler_processes(
diff --git a/python/sglang/srt/layers/moe/token_dispatcher/mooncake.py b/python/sglang/srt/layers/moe/token_dispatcher/mooncake.py
index 5fbf494e3..0a8d9ec55 100644
--- a/python/sglang/srt/layers/moe/token_dispatcher/mooncake.py
+++ b/python/sglang/srt/layers/moe/token_dispatcher/mooncake.py
@@ -231,7 +231,7 @@ class _MooncakeEPDispatcherImpl:
         use_fp8: bool = False,
     ):
         buffer = self._get_buffer()
-        active_ranks = ElasticEPStateManager.instance().active_ranks
+        active_ranks = self._get_active_ranks()
         packed_recv_hidden, packed_recv_count, self.handle, event, hook = (
             buffer.dispatch(
                 hidden_states,
@@ -271,7 +271,7 @@ class _MooncakeEPDispatcherImpl:
         topk_weights: torch.Tensor,
     ):
         buffer = self._get_buffer()
-        active_ranks = ElasticEPStateManager.instance().active_ranks
+        active_ranks = self._get_active_ranks()
         combined_hidden_states, event, hook = buffer.combine(
             hidden_states,
             topk_ids,
@@ -286,6 +286,12 @@ class _MooncakeEPDispatcherImpl:
         self.handle = None
         return combined_hidden_states, event, hook
 
+    def _get_active_ranks(self) -> torch.Tensor:
+        elastic_state = ElasticEPStateManager.instance()
+        if elastic_state is not None:
+            return elastic_state.active_ranks
+        return torch.ones(self.group.size(), dtype=torch.int32, device="cuda")
+
     def _get_buffer(self):
         return EPBuffer.get_ep_buffer(
             self.group,
diff --git a/python/sglang/srt/server_args.py b/python/sglang/srt/server_args.py
index 7b584d561..bd850b330 100644
--- a/python/sglang/srt/server_args.py
+++ b/python/sglang/srt/server_args.py
@@ -8179,6 +8179,11 @@ class ServerArgs:
                 "(--weight-cache-mode off) for this configuration."
             )
 
+        if self.weight_cache_mode != "off" and self.enable_eplb:
+            raise ValueError(
+                "--weight-cache-mode is not supported together with --enable-eplb."
+            )
+
     def _is_mistral_native_format(self) -> bool:
         """True iff the checkpoint requires load_format=mistral.
 
diff --git a/python/sglang/srt/weight_cache/daemon.py b/python/sglang/srt/weight_cache/daemon.py
index 5c6c0e1f5..d14d79075 100644
--- a/python/sglang/srt/weight_cache/daemon.py
+++ b/python/sglang/srt/weight_cache/daemon.py
@@ -34,19 +34,22 @@ Usage:
         --dist-init-method tcp://127.0.0.1:29500
 """
 
+import argparse
+import dataclasses
 import logging
+import multiprocessing
 import os
 import signal
 import socket
 import time
-from typing import Any, Dict, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
 
 import torch
 import torch.distributed as dist
 
 from sglang.srt.configs.load_config import LoadConfig
 from sglang.srt.platforms import current_platform
-from sglang.srt.runtime_context import publish
+from sglang.srt.runtime_context import get_parallel, publish
 
 from .protocol import (
     CacheConfig,
@@ -66,6 +69,9 @@ from .transport import choose_daemon_transport_backend
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from sglang.srt.server_args import ServerArgs
+
 # Per-connection timeout for the serial serve loop. A client exchange is tiny
 # (a config dict + IPC handle metadata), so this generous bound never trips a
 # healthy client, yet guarantees one hung/dead peer can't stall the other
@@ -73,6 +79,59 @@ logger = logging.getLogger(__name__)
 CLIENT_CONNECTION_TIMEOUT = 30.0
 
 
+@dataclasses.dataclass
+class WeightCacheDaemonArgs:
+    """Daemon-private worker identity and standalone launcher controls.
+
+    ``gpu_id``, ``tp_rank``, ``pp_rank``, and ``dist_init_method`` identify a
+    worker after the shared server configuration has been resolved. ``timeout``
+    and ``force`` apply only when this entrypoint launches and monitors a local
+    daemon group. None has a corresponding ServerArgs field with these
+    per-worker semantics.
+    """
+
+    gpu_id: Optional[int] = None
+    tp_rank: Optional[int] = None
+    pp_rank: int = 0
+    dist_init_method: Optional[str] = None
+    timeout: int = 1800
+    force: bool = False
+
+    @staticmethod
+    def add_cli_args(parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--gpu-id",
+            type=int,
+            default=None,
+            help="GPU device ID for a single daemon. If omitted, launches all local ranks.",
+        )
+        parser.add_argument(
+            "--tp-rank",
+            type=int,
+            default=None,
+            help="TP rank for a single daemon. If omitted, launches all local ranks.",
+        )
+        parser.add_argument("--pp-rank", type=int, default=0)
+        parser.add_argument(
+            "--dist-init-method",
+            default=None,
+            help="Daemon distributed init method (for example tcp://host:29500).",
+        )
+        parser.add_argument("--timeout", type=int, default=1800)
+        parser.add_argument("--force", action="store_true")
+
+    @classmethod
+    def from_cli_args(cls, args: argparse.Namespace) -> "WeightCacheDaemonArgs":
+        return cls(
+            gpu_id=args.gpu_id,
+            tp_rank=args.tp_rank,
+            pp_rank=args.pp_rank,
+            dist_init_method=args.dist_init_method,
+            timeout=args.timeout,
+            force=args.force,
+        )
+
+
 class WeightCacheDaemon:
     """Persistent GPU weight cache for a single TP rank.
 
@@ -82,42 +141,42 @@ class WeightCacheDaemon:
 
     def __init__(
         self,
-        model_path: str,
+        server_args: "ServerArgs",
         gpu_id: int,
-        tp_size: int = 1,
-        tp_rank: int = 0,
-        pp_size: int = 1,
-        pp_rank: int = 0,
-        dp_size: int = 1,
-        ep_size: int = 1,
-        load_format: str = "auto",
-        dtype: str = "auto",
-        quantization: Optional[str] = None,
-        model_loader_extra_config: str = "{}",
-        trust_remote_code: bool = False,
-        revision: Optional[str] = None,
+        tp_rank: int,
+        pp_rank: int,
         dist_init_method: Optional[str] = None,
     ):
-        self.model_path = model_path
+        self.server_args = server_args
+        self.model_path = server_args.model_path
         self.gpu_id = gpu_id
-        self.tp_size = tp_size
+        self.tp_size = server_args.tp_size
         self.tp_rank = tp_rank
-        self.pp_size = pp_size
+        self.pp_size = server_args.pp_size
         self.pp_rank = pp_rank
-        self.dp_size = dp_size
-        self.ep_size = ep_size
-        self.load_format = load_format
-        self.dtype = dtype
-        self.quantization = quantization
-        self.model_loader_extra_config = model_loader_extra_config
-        self.trust_remote_code = trust_remote_code
-        self.revision = revision
+        self.dp_size = server_args.dp_size
+        self.ep_size = server_args.ep_size
+        self.moe_dp_size = server_args.moe_dp_size
+        self.enable_dp_attention = server_args.enable_dp_attention
+        self.enable_dp_lm_head = server_args.enable_dp_lm_head
+        self.attn_cp_size = server_args.attn_cp_size
+        self.moe_dense_tp_size = server_args.moe_dense_tp_size
+        self.moe_a2a_backend = server_args.moe_a2a_backend
+        self.deepep_mode = server_args.deepep_mode
+        self.load_format = server_args.load_format
+        self.dtype = server_args.dtype
+        self.quantization = server_args.quantization
+        self.model_loader_extra_config = server_args.model_loader_extra_config
+        self.trust_remote_code = server_args.trust_remote_code
+        self.revision = server_args.revision
         self.dist_init_method = dist_init_method
 
         self.socket_path = get_socket_path(
-            compute_global_rank(tp_size, pp_rank, tp_rank)
+            compute_global_rank(self.tp_size, pp_rank, tp_rank)
+        )
+        self.ready_path = get_ready_path(
+            compute_global_rank(self.tp_size, pp_rank, tp_rank)
         )
-        self.ready_path = get_ready_path(compute_global_rank(tp_size, pp_rank, tp_rank))
 
         self.model = None
         self.config: Optional[CacheConfig] = None
@@ -164,12 +223,18 @@ class WeightCacheDaemon:
                 distributed_init_method=self.dist_init_method,
                 local_rank=self.gpu_id,
                 backend=current_platform.get_torch_distributed_backend_str(),
+                moe_a2a_backend=server_args.moe_a2a_backend,
             )
 
         initialize_model_parallel(
             tensor_model_parallel_size=self.tp_size,
             pipeline_model_parallel_size=self.pp_size,
             expert_model_parallel_size=self.ep_size,
+            attention_data_parallel_size=(
+                self.dp_size if self.enable_dp_attention else 1
+            ),
+            attention_context_model_parallel_size=self.attn_cp_size,
+            moe_data_model_parallel_size=self.moe_dp_size,
         )
 
         # Initialize DP attention state (required by some models like Qwen3 MoE)
@@ -210,22 +275,14 @@ class WeightCacheDaemon:
         from sglang.srt.configs.device_config import DeviceConfig
         from sglang.srt.configs.model_config import ModelConfig
         from sglang.srt.model_loader.loader import get_model_loader
-        from sglang.srt.server_args import ServerArgs
 
-        server_args = ServerArgs(
-            model_path=self.model_path,
-            dtype=self.dtype,
-            quantization=self.quantization,
-            trust_remote_code=self.trust_remote_code,
-            tp_size=self.tp_size,
-            pp_size=self.pp_size,
-            dp_size=self.dp_size,
-            ep_size=self.ep_size,
-            load_format=self.load_format,
-            model_loader_extra_config=self.model_loader_extra_config,
-        )
+        server_args = self.server_args
         publish(server_args, role="weight_cache_daemon")
 
+        from sglang.srt.layers.moe import initialize_moe_config
+
+        initialize_moe_config(server_args)
+
         # Initialize distributed backend for model loading
         # (must be done after server_args and model_config are available)
         # Build model config first, then init distributed
@@ -237,7 +294,7 @@ class WeightCacheDaemon:
             quantization=self.quantization,
         )
 
-        # Build cache config fingerprint BEFORE loading the model.
+        # Build cache config fingerprint before loading the model.
         # Loading may mutate hf_config.quantization_config (e.g. via
         # process_weights_after_loading), which would produce a different
         # hash than what the engine computes from the original config.
@@ -250,6 +307,15 @@ class WeightCacheDaemon:
         if not quant_method and quant_config is not None:
             quant_method = get_quant_method_name(quant_config)
 
+        # Refuse unsupported quant methods before creating distributed groups
+        # or touching model weights.
+        check_ipc_quant_support(quant_method, quant_config, where="daemon")
+
+        # The initialized groups are the authority for rank identity. This
+        # avoids maintaining a second copy of the model-parallel hierarchy.
+        self._init_distributed(server_args, model_config)
+        moe_dp_rank = get_parallel().moe_dp_rank
+        moe_ep_rank = get_parallel().moe_ep_rank
         self.config = CacheConfig(
             model_path=self.model_path,
             model_arch=(
@@ -263,6 +329,14 @@ class WeightCacheDaemon:
             pp_rank=self.pp_rank,
             dp_size=self.dp_size,
             ep_size=self.ep_size,
+            moe_dp_size=self.moe_dp_size,
+            moe_dp_rank=moe_dp_rank,
+            moe_ep_rank=moe_ep_rank,
+            enable_dp_attention=self.enable_dp_attention,
+            enable_dp_lm_head=self.enable_dp_lm_head,
+            attn_cp_size=self.attn_cp_size,
+            moe_dense_tp_size=self.moe_dense_tp_size,
+            moe_a2a_backend=self.moe_a2a_backend,
             quant_method=quant_method,
             quant_config_hash=hash_quant_config(quant_config),
             dtype=str(model_config.dtype),
@@ -270,14 +344,6 @@ class WeightCacheDaemon:
             **compute_env_stamp(),
         )
 
-        # Refuse to serve quant methods not verified to round-trip through pure
-        # IPC tensor export. Checked before loading so an unsupported model
-        # fails fast instead of after minutes of disk I/O.
-        check_ipc_quant_support(quant_method, quant_config, where="daemon")
-
-        # Initialize distributed backend (requires server_args + model_config)
-        self._init_distributed(server_args, model_config)
-
         # Build load config
         load_config = LoadConfig(
             load_format=self.load_format,
@@ -523,20 +589,10 @@ class WeightCacheDaemon:
 
 
 def run_weight_cache_daemon(
-    model_path: str,
+    server_args: "ServerArgs",
     gpu_id: int,
-    tp_size: int = 1,
-    tp_rank: int = 0,
-    pp_size: int = 1,
-    pp_rank: int = 0,
-    dp_size: int = 1,
-    ep_size: int = 1,
-    load_format: str = "auto",
-    dtype: str = "auto",
-    quantization: Optional[str] = None,
-    model_loader_extra_config: str = "{}",
-    trust_remote_code: bool = False,
-    revision: Optional[str] = None,
+    tp_rank: int,
+    pp_rank: int,
     dist_init_method: Optional[str] = None,
 ):
     """Entry point for running a weight cache daemon process."""
@@ -554,20 +610,10 @@ def run_weight_cache_daemon(
     kill_itself_when_parent_died()
 
     daemon = WeightCacheDaemon(
-        model_path=model_path,
+        server_args=server_args,
         gpu_id=gpu_id,
-        tp_size=tp_size,
         tp_rank=tp_rank,
-        pp_size=pp_size,
         pp_rank=pp_rank,
-        dp_size=dp_size,
-        ep_size=ep_size,
-        load_format=load_format,
-        dtype=dtype,
-        quantization=quantization,
-        model_loader_extra_config=model_loader_extra_config,
-        trust_remote_code=trust_remote_code,
-        revision=revision,
         dist_init_method=dist_init_method,
     )
 
@@ -575,22 +621,26 @@ def run_weight_cache_daemon(
     daemon.serve()
 
 
+def spawn_weight_cache_daemon(
+    server_args: "ServerArgs",
+    *,
+    gpu_id: int,
+    tp_rank: int,
+    pp_rank: int,
+    dist_init_method: str,
+):
+    """Start one daemon from the complete resolved server configuration."""
+    ctx = multiprocessing.get_context("spawn")
+    proc = ctx.Process(
+        target=run_weight_cache_daemon,
+        args=(server_args, gpu_id, tp_rank, pp_rank, dist_init_method),
+    )
+    proc.start()
+    return proc
+
+
 def launch_weight_cache_daemons(
-    model_path: str,
-    tp_size: int = 1,
-    pp_size: int = 1,
-    dp_size: int = 1,
-    ep_size: int = 1,
-    nnodes: int = 1,
-    node_rank: int = 0,
-    base_gpu_id: int = 0,
-    gpu_id_step: int = 1,
-    load_format: str = "auto",
-    dtype: str = "auto",
-    quantization: Optional[str] = None,
-    model_loader_extra_config: str = "{}",
-    trust_remote_code: bool = False,
-    revision: Optional[str] = None,
+    server_args: "ServerArgs",
     dist_init_method: Optional[str] = None,
     timeout: int = 1800,
     force: bool = False,
@@ -601,9 +651,9 @@ def launch_weight_cache_daemons(
     For multi-node (nnodes>1): spawns this node's share of PP×TP daemons,
     mapping local gpu_id to the correct global (pp_rank, tp_rank).
 
-    Uses subprocess.Popen instead of multiprocessing.Process to avoid
-    initializing CUDA in the parent process, which can degrade CUDA IPC
-    performance in child processes.
+    Uses ``multiprocessing`` with the ``spawn`` start method. The child starts
+    in a clean interpreter and receives the complete resolved ``ServerArgs``
+    through pickle, avoiding a second hand-maintained CLI configuration path.
 
     Usage (single-node):
         python -m sglang.srt.weight_cache.daemon \\
@@ -623,24 +673,22 @@ def launch_weight_cache_daemons(
             --dist-init-method tcp://node0-ip:29500
     """
     import socket as sock_mod
-    import subprocess
-    import sys
 
     # Replicate _calculate_rank_ranges logic from engine.py
-    pp_size_per_node = max(pp_size // nnodes, 1)
-    nnodes_per_pp_rank = max(nnodes // pp_size, 1)
+    pp_size_per_node = max(server_args.pp_size // server_args.nnodes, 1)
+    nnodes_per_pp_rank = max(server_args.nnodes // server_args.pp_size, 1)
     pp_rank_range = range(
-        pp_size_per_node * (node_rank // nnodes_per_pp_rank),
-        pp_size_per_node * (node_rank // nnodes_per_pp_rank + 1),
+        pp_size_per_node * (server_args.node_rank // nnodes_per_pp_rank),
+        pp_size_per_node * (server_args.node_rank // nnodes_per_pp_rank + 1),
     )
     nnodes_per_tp_group = nnodes_per_pp_rank
-    tp_size_per_node = tp_size // nnodes_per_tp_group
+    tp_size_per_node = server_args.tp_size // nnodes_per_tp_group
     tp_rank_range = range(
-        tp_size_per_node * (node_rank % nnodes_per_tp_group),
-        tp_size_per_node * (node_rank % nnodes_per_tp_group + 1),
+        tp_size_per_node * (server_args.node_rank % nnodes_per_tp_group),
+        tp_size_per_node * (server_args.node_rank % nnodes_per_tp_group + 1),
     )
 
-    if nnodes > 1 and dist_init_method is None:
+    if server_args.nnodes > 1 and dist_init_method is None:
         raise ValueError(
             "dist_init_method is required for multi-node weight cache daemons. "
             "Use --dist-init-method tcp://<node0-ip>:<port> to specify the "
@@ -654,13 +702,10 @@ def launch_weight_cache_daemons(
             free_port = s.getsockname()[1]
         dist_init_method = f"tcp://127.0.0.1:{free_port}"
 
-    python_path = sys.executable
-    daemon_module = "sglang.srt.weight_cache.daemon"
-
     # Validate and clean up stale .ready/.sock files from prior runs.
     for pp_rank in pp_rank_range:
         for tp_rank in tp_rank_range:
-            global_rank = compute_global_rank(tp_size, pp_rank, tp_rank)
+            global_rank = compute_global_rank(server_args.tp_size, pp_rank, tp_rank)
             cleanup_stale_daemon_files(global_rank, force=force)
 
     procs = []
@@ -671,46 +716,16 @@ def launch_weight_cache_daemons(
                 tp_rank,
                 pp_size_per_node,
                 tp_size_per_node,
-                base_gpu_id=base_gpu_id,
-                gpu_id_step=gpu_id_step,
+                base_gpu_id=server_args.base_gpu_id,
+                gpu_id_step=server_args.gpu_id_step,
+            )
+            proc = spawn_weight_cache_daemon(
+                server_args,
+                gpu_id=gpu_id,
+                tp_rank=tp_rank,
+                pp_rank=pp_rank,
+                dist_init_method=dist_init_method,
             )
-            cmd = [
-                python_path,
-                "-m",
-                daemon_module,
-                "--model-path",
-                model_path,
-                "--gpu-id",
-                str(gpu_id),
-                "--tp-size",
-                str(tp_size),
-                "--tp-rank",
-                str(tp_rank),
-                "--dp-size",
-                str(dp_size),
-                "--ep-size",
-                str(ep_size),
-                "--pp-size",
-                str(pp_size),
-                "--pp-rank",
-                str(pp_rank),
-                "--load-format",
-                load_format,
-                "--dtype",
-                dtype,
-                "--dist-init-method",
-                dist_init_method,
-            ]
-            if quantization:
-                cmd += ["--quantization", quantization]
-            if model_loader_extra_config and model_loader_extra_config != "{}":
-                cmd += ["--model-loader-extra-config", model_loader_extra_config]
-            if trust_remote_code:
-                cmd += ["--trust-remote-code"]
-            if revision:
-                cmd += ["--revision", revision]
-
-            proc = subprocess.Popen(cmd)
             procs.append(proc)
             logger.info(
                 f"Launched weight cache daemon gpu={gpu_id} "
@@ -723,7 +738,7 @@ def launch_weight_cache_daemons(
     start_time = time.time()
     for pp_rank in pp_rank_range:
         for tp_rank in tp_rank_range:
-            global_rank = compute_global_rank(tp_size, pp_rank, tp_rank)
+            global_rank = compute_global_rank(server_args.tp_size, pp_rank, tp_rank)
             ready_path = get_ready_path(global_rank)
             while not os.path.exists(ready_path):
                 time.sleep(check_interval)
@@ -740,39 +755,38 @@ def launch_weight_cache_daemons(
                     )
                 # Check if any daemon exited prematurely
                 for p in procs:
-                    retcode = p.poll()
-                    if retcode is not None:
+                    if not p.is_alive():
                         logger.error(
                             f"Weight cache daemon exited prematurely "
-                            f"with code {retcode}"
+                            f"with code {p.exitcode}"
                         )
                         for other in procs:
-                            if other.poll() is None:
+                            if other.is_alive():
                                 other.terminate()
                         raise RuntimeError(
                             f"Weight cache daemon exited prematurely "
-                            f"with code {retcode}"
+                            f"with code {p.exitcode}"
                         )
             logger.info(
                 f"Weight cache daemon pp_rank={pp_rank} tp_rank={tp_rank} is ready"
             )
 
     logger.info(
-        f"All {num_daemons} weight cache daemons on node {node_rank} are ready "
+        f"All {num_daemons} weight cache daemons on node {server_args.node_rank} are ready "
         f"(pp_ranks={pp_rank_range.start}..{pp_rank_range.stop - 1}, "
         f"tp_ranks={tp_rank_range.start}..{tp_rank_range.stop - 1}, "
         f"dist_init_method={dist_init_method})"
     )
 
-    # Monitor daemons — poll all of them and, the moment any one exits,
-    # terminate the rest and raise. A serial proc.wait() would not notice a
+    # Monitor daemons and, the moment any one exits, terminate the rest and
+    # raise. A serial proc.join() would not notice a
     # mid-list death (e.g. procs[1] dying while procs[0] is still alive) until
     # the earlier proc happened to exit, and it never surfaced the failure.
     exited = None
     try:
         while exited is None:
             for proc in procs:
-                if proc.poll() is not None:
+                if not proc.is_alive():
                     exited = proc
                     break
             else:
@@ -782,149 +796,57 @@ def launch_weight_cache_daemons(
         logger.info("Received KeyboardInterrupt, shutting down daemons")
     finally:
         for proc in procs:
-            if proc.poll() is None:
+            if proc.is_alive():
                 proc.terminate()
         for proc in procs:
-            try:
-                proc.wait(timeout=5)
-            except subprocess.TimeoutExpired:
+            proc.join(timeout=5)
+            if proc.is_alive():
                 proc.kill()
+                proc.join()
         logger.info("All weight cache daemons have been terminated")
 
     if exited is not None:
         raise RuntimeError(
             f"Weight cache daemon (pid={exited.pid}) exited with code "
-            f"{exited.returncode}; terminated the remaining daemons."
+            f"{exited.exitcode}; terminated the remaining daemons."
         )
 
 
 if __name__ == "__main__":
-    import argparse
+    worker_parser = argparse.ArgumentParser(add_help=False)
+    WeightCacheDaemonArgs.add_cli_args(worker_parser)
+    worker_ns, server_argv = worker_parser.parse_known_args()
 
-    parser = argparse.ArgumentParser(description="SGLang Weight Cache Daemon")
-    parser.add_argument("--model-path", required=True, help="Path to model weights")
-    parser.add_argument("--tp-size", type=int, default=1, help="Tensor parallel size")
-    parser.add_argument(
-        "--gpu-id",
-        type=int,
-        default=None,
-        help="GPU device ID for a single daemon. "
-        "If omitted, launches daemons for all TP ranks (0..tp_size-1).",
-    )
-    parser.add_argument(
-        "--tp-rank",
-        type=int,
-        default=None,
-        help="TP rank for a single daemon. "
-        "If omitted, launches daemons for all TP ranks.",
-    )
-    parser.add_argument("--dp-size", type=int, default=1, help="Data parallel size")
-    parser.add_argument("--ep-size", type=int, default=1, help="Expert parallel size")
-    parser.add_argument("--pp-size", type=int, default=1, help="Pipeline parallel size")
-    parser.add_argument("--pp-rank", type=int, default=0, help="Pipeline parallel rank")
-    parser.add_argument("--nnodes", type=int, default=1, help="Total number of nodes")
-    parser.add_argument(
-        "--base-gpu-id",
-        type=int,
-        default=0,
-        help="GPU id of this node's first rank (mirrors the engine's "
-        "--base-gpu-id). Used to place daemons on the same GPUs the engine "
-        "ranks will use.",
-    )
-    parser.add_argument(
-        "--gpu-id-step",
-        type=int,
-        default=1,
-        help="Stride between consecutive ranks' GPU ids (mirrors the engine's "
-        "--gpu-id-step).",
-    )
-    parser.add_argument(
-        "--node-rank",
-        type=int,
-        default=0,
-        help="Rank of this node (0-indexed). Required for multi-node.",
-    )
-    parser.add_argument("--load-format", default="auto", help="Weight load format")
-    parser.add_argument("--dtype", default="auto", help="Model dtype")
-    parser.add_argument("--quantization", default=None, help="Quantization method")
-    parser.add_argument(
-        "--model-loader-extra-config",
-        default="{}",
-        help="Extra config for model loader (JSON string)",
-    )
-    parser.add_argument("--trust-remote-code", action="store_true")
-    parser.add_argument("--revision", default=None, help="Model revision")
-    parser.add_argument(
-        "--dist-init-method",
-        default=None,
-        help="Distributed init method (e.g. tcp://node0-ip:29500). "
-        "Auto-assigned for single-node when launching all ranks. "
-        "Required for multi-node (nnodes > 1) and must be accessible "
-        "from all nodes. Also required for tp_size > 1 when launching "
-        "a single rank.",
-    )
-    parser.add_argument(
-        "--timeout",
-        type=int,
-        default=1800,
-        help="Timeout in seconds to wait for all daemons to become ready (default: 1800)",
-    )
-    parser.add_argument(
-        "--force",
-        action="store_true",
-        help="Take over a rank whose .ready file still points at a live PID by "
-        "killing that daemon (use to reclaim a wedged/orphaned daemon).",
-    )
+    from sglang.srt.server_args import prepare_server_args
 
-    args = parser.parse_args()
-
-    if args.gpu_id is not None or args.tp_rank is not None:
-        # Single-rank mode: launch one daemon for the specified rank
-        gpu_id = args.gpu_id if args.gpu_id is not None else args.tp_rank
-        tp_rank = args.tp_rank if args.tp_rank is not None else args.gpu_id
-        # Refuse to clobber a live daemon already holding this rank (mirrors the
-        # multi-rank launcher and the engine path, which the multi-rank spawns
-        # of this same entrypoint rely on). --force kills and takes over.
+    server_args = prepare_server_args(server_argv)
+    daemon_args = WeightCacheDaemonArgs.from_cli_args(worker_ns)
+    if daemon_args.gpu_id is not None or daemon_args.tp_rank is not None:
+        gpu_id = (
+            daemon_args.gpu_id
+            if daemon_args.gpu_id is not None
+            else daemon_args.tp_rank
+        )
+        tp_rank = (
+            daemon_args.tp_rank
+            if daemon_args.tp_rank is not None
+            else daemon_args.gpu_id
+        )
         cleanup_stale_daemon_files(
-            compute_global_rank(args.tp_size, args.pp_rank, tp_rank),
-            force=args.force,
+            compute_global_rank(server_args.tp_size, daemon_args.pp_rank, tp_rank),
+            force=daemon_args.force,
         )
         run_weight_cache_daemon(
-            model_path=args.model_path,
+            server_args,
             gpu_id=gpu_id,
-            tp_size=args.tp_size,
             tp_rank=tp_rank,
-            pp_size=args.pp_size,
-            pp_rank=args.pp_rank,
-            dp_size=args.dp_size,
-            ep_size=args.ep_size,
-            load_format=args.load_format,
-            dtype=args.dtype,
-            quantization=args.quantization,
-            model_loader_extra_config=args.model_loader_extra_config,
-            trust_remote_code=args.trust_remote_code,
-            revision=args.revision,
-            dist_init_method=args.dist_init_method,
+            pp_rank=daemon_args.pp_rank,
+            dist_init_method=daemon_args.dist_init_method,
         )
     else:
-        # Multi-rank mode: launch daemons for this node's TP ranks
         launch_weight_cache_daemons(
-            model_path=args.model_path,
-            tp_size=args.tp_size,
-            pp_size=args.pp_size,
-            dp_size=args.dp_size,
-            ep_size=args.ep_size,
-            nnodes=args.nnodes,
-            node_rank=args.node_rank,
-            base_gpu_id=args.base_gpu_id,
-            gpu_id_step=args.gpu_id_step,
-            load_format=args.load_format,
-            dtype=args.dtype,
-            quantization=args.quantization,
-            model_loader_extra_config=args.model_loader_extra_config,
-            trust_remote_code=args.trust_remote_code,
-            revision=args.revision,
-            dist_init_method=args.dist_init_method,
-            timeout=args.timeout,
-            force=args.force,
+            server_args,
+            dist_init_method=daemon_args.dist_init_method,
+            timeout=daemon_args.timeout,
+            force=daemon_args.force,
         )
diff --git a/python/sglang/srt/weight_cache/ipc_loader.py b/python/sglang/srt/weight_cache/ipc_loader.py
index a649b7fdd..5e6c942bf 100644
--- a/python/sglang/srt/weight_cache/ipc_loader.py
+++ b/python/sglang/srt/weight_cache/ipc_loader.py
@@ -478,7 +478,7 @@ class IpcModelLoader(BaseModelLoader):
 
         try:
             # Build engine's config fingerprint
-            from sglang.srt.runtime_context import get_parallel
+            from sglang.srt.runtime_context import get_exec, get_parallel
 
             ps = get_parallel()
             tp_size = ps.tp_size
@@ -488,8 +488,11 @@ class IpcModelLoader(BaseModelLoader):
             pp_rank = ps.pp_rank
 
             ep_size = ps.moe_ep_size
+            moe_dp_size = ps.moe_dp_size
+            moe_dp_rank = ps.moe_dp_rank
+            moe_ep_rank = ps.moe_ep_rank
 
-            dp_size = get_parallel().dp_size
+            dp_size = ps.dp_size
 
             quant_method, quant_config = self._resolve_engine_quant(model_config)
 
@@ -506,6 +509,14 @@ class IpcModelLoader(BaseModelLoader):
                 pp_rank=pp_rank,
                 dp_size=dp_size,
                 ep_size=ep_size,
+                moe_dp_size=moe_dp_size,
+                moe_dp_rank=moe_dp_rank,
+                moe_ep_rank=moe_ep_rank,
+                enable_dp_attention=ps.enable_dp_attention,
+                enable_dp_lm_head=ps.enable_dp_lm_head,
+                attn_cp_size=ps.attn_cp_size,
+                moe_dense_tp_size=ps.moe_dense_tp_size,
+                moe_a2a_backend=get_exec().moe.moe_a2a_backend,
                 quant_method=quant_method,
                 quant_config_hash=hash_quant_config(quant_config),
                 dtype=str(model_config.dtype),
diff --git a/python/sglang/srt/weight_cache/protocol.py b/python/sglang/srt/weight_cache/protocol.py
index 8b3b4b341..befdf8e1e 100644
--- a/python/sglang/srt/weight_cache/protocol.py
+++ b/python/sglang/srt/weight_cache/protocol.py
@@ -42,6 +42,14 @@ class CacheConfig(msgspec.Struct):
     pp_rank: int
     dp_size: int
     ep_size: int
+    moe_dp_size: int
+    moe_dp_rank: int
+    moe_ep_rank: int
+    enable_dp_attention: bool
+    enable_dp_lm_head: bool
+    attn_cp_size: int
+    moe_dense_tp_size: Optional[int]
+    moe_a2a_backend: str
     quant_method: str  # e.g. "fp8", "gptq_marlin", "" for unquantized
     quant_config_hash: str  # SHA-256 hash of quantization config
     dtype: str  # e.g. "torch.float16"
diff --git a/test/manual/test_weight_cache_e2e.py b/test/manual/test_weight_cache_e2e.py
index 562820497..5cd757f11 100644
--- a/test/manual/test_weight_cache_e2e.py
+++ b/test/manual/test_weight_cache_e2e.py
@@ -54,20 +54,24 @@ def run_single_daemon(
     """Run a single daemon process for one (pp_rank, tp_rank)."""
     import traceback
 
+    from sglang.srt.server_args import ServerArgs
     from sglang.srt.weight_cache.daemon import WeightCacheDaemon
 
-    daemon = WeightCacheDaemon(
+    server_args = ServerArgs(
         model_path=model_path,
-        gpu_id=gpu_id,
         tp_size=tp_size,
-        tp_rank=tp_rank,
         pp_size=pp_size,
-        pp_rank=pp_rank,
         dp_size=1,
         load_format=load_format,
         dtype=dtype,
         quantization=quantization,
         trust_remote_code=trust_remote_code,
+    )
+    daemon = WeightCacheDaemon(
+        server_args=server_args,
+        gpu_id=gpu_id,
+        tp_rank=tp_rank,
+        pp_rank=pp_rank,
         dist_init_method=dist_init_method,
     )
     daemon.socket_path = socket_path
diff --git a/test/registered/unit/model_loader/test_weight_cache_protocol.py b/test/registered/unit/model_loader/test_weight_cache_protocol.py
index ae4b4354c..d130f98f0 100644
--- a/test/registered/unit/model_loader/test_weight_cache_protocol.py
+++ b/test/registered/unit/model_loader/test_weight_cache_protocol.py
@@ -7,7 +7,7 @@ These cover the pure-Python logic that the GPU end-to-end test
   - length-prefixed socket framing (send_msg/recv_msg) over socketpair()
   - CacheConfig fingerprint matching / (de)serialization
   - quant-config hashing and method-name extraction
-  - the daemon rank formula and socket/ready path derivation
+  - daemon spawn configuration and socket/ready path derivation
   - the IPC quantization allowlist (the gate that keeps silently-wrong
     quant methods off the zero-copy path)
   - stale-vs-live daemon file cleanup
@@ -21,6 +21,7 @@ import os
 import socket
 import struct
 import unittest
+from types import SimpleNamespace
 
 import torch
 
@@ -61,6 +62,14 @@ def _make_cache_config(**overrides) -> CacheConfig:
         pp_rank=0,
         dp_size=1,
         ep_size=1,
+        moe_dp_size=1,
+        moe_dp_rank=0,
+        moe_ep_rank=0,
+        enable_dp_attention=False,
+        enable_dp_lm_head=False,
+        attn_cp_size=1,
+        moe_dense_tp_size=None,
+        moe_a2a_backend="none",
         quant_method="",
         quant_config_hash="",
         dtype="torch.float16",
@@ -159,6 +168,11 @@ class TestCacheConfig(CustomTestCase):
         base = _make_cache_config()
         for field, value in (
             ("tp_rank", 1),
+            ("moe_dp_rank", 1),
+            ("moe_ep_rank", 1),
+            ("enable_dp_attention", True),
+            ("moe_dense_tp_size", 1),
+            ("moe_a2a_backend", "mooncake"),
             ("dtype", "torch.bfloat16"),
             ("quant_method", "fp8"),
             ("model_path", "/models/other"),
@@ -250,6 +264,68 @@ class TestGlobalRankAndPaths(CustomTestCase):
         )
 
 
+class TestDaemonLaunchConfiguration(CustomTestCase):
+    def test_spawn_forwards_complete_server_args_without_projection(self):
+        from sglang.srt.weight_cache import daemon
+
+        # The spawn helper receives Engine's already-resolved ServerArgs. A
+        # minimal namespace keeps this projection test CPU-only and
+        # model-independent; importantly, no EPLB configuration is involved.
+        server_args = SimpleNamespace(
+            model_path="/models/demo",
+            tp_size=8,
+            pp_size=1,
+            dp_size=8,
+            ep_size=8,
+            moe_dp_size=2,
+            enable_dp_attention=True,
+            enable_dp_lm_head=True,
+            attn_cp_size=2,
+            moe_dense_tp_size=1,
+            moe_a2a_backend="mooncake",
+            deepep_mode="low_latency",
+            load_format="safetensors",
+            dtype="bfloat16",
+            quantization="fp8",
+            model_loader_extra_config='{"key": "value"}',
+            trust_remote_code=True,
+            revision="test-revision",
+        )
+
+        class FakeProcess:
+            pid = 1234
+
+            def start(self):
+                pass
+
+        class FakeContext:
+            def Process(self, **kwargs):
+                self.kwargs = kwargs
+                return FakeProcess()
+
+        fake_context = FakeContext()
+        from unittest import mock
+
+        with mock.patch.object(
+            daemon.multiprocessing, "get_context", return_value=fake_context
+        ) as get_context:
+            result = daemon.spawn_weight_cache_daemon(
+                server_args,
+                gpu_id=3,
+                tp_rank=3,
+                pp_rank=0,
+                dist_init_method="tcp://127.0.0.1:29500",
+            )
+
+        get_context.assert_called_once_with("spawn")
+        self.assertIsInstance(result, FakeProcess)
+        self.assertIs(fake_context.kwargs["target"], daemon.run_weight_cache_daemon)
+        self.assertEqual(
+            fake_context.kwargs["args"],
+            (server_args, 3, 3, 0, "tcp://127.0.0.1:29500"),
+        )
+
+
 class TestIpcQuantAllowlist(CustomTestCase):
     def test_unquantized_is_supported(self):
         self.assertTrue(is_ipc_quant_supported("", None))
diff --git a/test/registered/unit/test_supplied_instance_exposure_ratchet.py b/test/registered/unit/test_supplied_instance_exposure_ratchet.py
index bc94d70ec..aa0dc8fa1 100644
--- a/test/registered/unit/test_supplied_instance_exposure_ratchet.py
+++ b/test/registered/unit/test_supplied_instance_exposure_ratchet.py
@@ -179,6 +179,22 @@ _EXPOSED = {
     ("utils/common.py", "speculative_num_draft_tokens"),
     ("utils/common.py", "speculative_num_steps"),
     ("utils/hf_transformers/processor.py", "image_processor_backend"),
+    # The daemon command and constructor snapshot the resolved startup layout
+    # before the daemon's loading lifecycle can apply any runtime overrides.
+    ("weight_cache/daemon.py", "attn_cp_size"),
+    ("weight_cache/daemon.py", "deepep_mode"),
+    ("weight_cache/daemon.py", "dp_size"),
+    ("weight_cache/daemon.py", "dtype"),
+    ("weight_cache/daemon.py", "enable_dp_attention"),
+    ("weight_cache/daemon.py", "enable_dp_lm_head"),
+    ("weight_cache/daemon.py", "ep_size"),
+    ("weight_cache/daemon.py", "load_format"),
+    ("weight_cache/daemon.py", "model_path"),
+    ("weight_cache/daemon.py", "moe_a2a_backend"),
+    ("weight_cache/daemon.py", "moe_dense_tp_size"),
+    ("weight_cache/daemon.py", "moe_dp_size"),
+    ("weight_cache/daemon.py", "pp_size"),
+    ("weight_cache/daemon.py", "quantization"),
 }
 
 # Pairs whose resolution write only happens on a CUDA host (capability or
@@ -196,6 +212,11 @@ _OVERRIDDEN_AND_READ = {
     ("dllm/config.py", "model_path"),
     ("entrypoints/engine.py", "reasoning_parser"),
     ("entrypoints/engine.py", "tool_call_parser"),
+    ("weight_cache/daemon.py", "dp_size"),
+    ("weight_cache/daemon.py", "dtype"),
+    ("weight_cache/daemon.py", "ep_size"),
+    ("weight_cache/daemon.py", "load_format"),
+    ("weight_cache/daemon.py", "model_path"),
     ("configs/model_config.py", "dtype"),
     ("configs/model_config.py", "model_path"),
     ("mem_cache/kv_cache_builder.py", "hicache_storage_backend"),