diff --git a/python/sglang/jit_kernel/csrc/lora/moe_lora_align_kernel.cu b/python/sglang/jit_kernel/csrc/lora/moe_lora_align_kernel.cu
index 5f6145286..3e365f801 100644
--- a/python/sglang/jit_kernel/csrc/lora/moe_lora_align_kernel.cu
+++ b/python/sglang/jit_kernel/csrc/lora/moe_lora_align_kernel.cu
@@ -253,7 +253,9 @@ SGL_DEVICE void _count_and_sort_expert_tokens(
 
   for (size_t i = tid; i < numel; i += stride) {
     int32_t expert_id = topk_ids[i];
-    if (expert_id >= num_experts) {
+    // Under EP, StandardDispatcher writes -1 for experts not owned by this
+    // rank; must filter the sentinel before indexing cumsum/sorted buffers.
+    if (expert_id < 0 || expert_id >= num_experts) {
       continue;
     }
 
diff --git a/python/sglang/srt/lora/mem_pool.py b/python/sglang/srt/lora/mem_pool.py
index 34444edfc..bda008068 100644
--- a/python/sglang/srt/lora/mem_pool.py
+++ b/python/sglang/srt/lora/mem_pool.py
@@ -1,10 +1,17 @@
 import logging
 import re
-from typing import Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
+from typing import Callable, Dict, Iterable, Iterator, List, Optional, Set, Tuple, Union
 
 import torch
 
-from sglang.srt.distributed import divide, get_pp_group
+from sglang.srt.distributed import (
+    divide,
+    get_moe_expert_parallel_rank,
+    get_moe_expert_parallel_world_size,
+    get_moe_tensor_parallel_rank,
+    get_moe_tensor_parallel_world_size,
+    get_pp_group,
+)
 from sglang.srt.lora.eviction_policy import get_eviction_policy
 from sglang.srt.lora.layers import BaseLayerWithLoRA
 from sglang.srt.lora.lora import LoRAAdapter
@@ -46,6 +53,43 @@ class EmptySlot:
 EMPTY_SLOT = EmptySlot()
 
 
+def _get_moe_ep_context() -> Tuple[int, int]:
+    """Return `(moe_ep_size, moe_ep_rank)`, or `(1, 0)` if the MoE EP group
+    is not initialized (hermetic tests or pure-TP launches)."""
+    try:
+        return get_moe_expert_parallel_world_size(), get_moe_expert_parallel_rank()
+    except Exception:  # pragma: no cover - MoE EP group not initialized
+        return 1, 0
+
+
+def _get_moe_tp_context() -> Tuple[int, int]:
+    """Return `(moe_tp_size, moe_tp_rank)`, or `(1, 0)` if the MoE TP group
+    is not initialized. Under `--tp N --ep N` the outer attention TP group
+    is consumed entirely by EP, leaving `moe_tp_size == 1`, so per-expert
+    MoE weights are NOT sharded along their inner dim even though attention
+    weights are."""
+    try:
+        return get_moe_tensor_parallel_world_size(), get_moe_tensor_parallel_rank()
+    except Exception:  # pragma: no cover - MoE TP group not initialized
+        return 1, 0
+
+
+def _moe_runner_keeps_global_expert_ids() -> bool:
+    """True if the active MoE runner keeps global `topk_ids` instead of
+    remapping to local IDs. Mirrors the predicate in `StandardDispatcher`."""
+    try:
+        from sglang.srt.layers.moe.utils import get_moe_runner_backend
+
+        b = get_moe_runner_backend()
+        return (
+            b.is_flashinfer_cutlass()
+            or b.is_flashinfer_cutedsl()
+            or b.is_flashinfer_trtllm_routed()
+        )
+    except Exception:  # pragma: no cover - backend not initialized
+        return False
+
+
 class LoRAMemoryPool:
     """Class for memory pool management of lora modules"""
 
@@ -76,6 +120,32 @@ class LoRAMemoryPool:
         self.experts_shared_outer_loras: bool = experts_shared_outer_loras
         self.strict_loading: bool = strict_loading
 
+        # Under EP with a Triton/DeepGEMM runner, `StandardDispatcher` remaps
+        # global `topk_ids` -> local expert IDs before the MoE kernel, so
+        # per-expert LoRA buffers must be sized and keyed by the local slice.
+        # FlashInfer CUTLASS/CuteDSL/TRTLLM-routed keep global IDs, and an
+        # uneven expert split (`num_experts % moe_ep_size != 0`, shouldn't
+        # happen in practice) is also treated as globally-keyed so we don't
+        # silently truncate experts.
+        self.moe_ep_size, self.moe_ep_rank = _get_moe_ep_context()
+        num_experts_global = self._get_num_experts(base_model)
+        self.moe_use_local_expert_ids = (
+            self.moe_ep_size > 1
+            and not _moe_runner_keeps_global_expert_ids()
+            and num_experts_global % self.moe_ep_size == 0
+        )
+
+        # Per-expert MoE weights are sharded by `moe_tp_size`, NOT the outer
+        # `tp_size`: `moe_tp_size = tp_size // ep_size // dp_size`, so under
+        # e.g. `--tp 4 --ep 4` each rank holds full-width expert weights
+        # (`moe_tp_size == 1`). Sizing per-expert LoRA buffers by `tp_size`
+        # here would yield a 4x-narrower inner dim than the adapter weight
+        # (which `FusedMoEWithLoRA.slice_moe_lora_{a,b}_weights` correctly
+        # skip-slices when `moe_tp_size <= 1`), producing a shape-mismatch
+        # assert during weight load. Non-MoE modules still shard by
+        # `tp_size` because attention TP is unchanged.
+        self.moe_tp_size, self.moe_tp_rank = _get_moe_tp_context()
+
         # Initialize eviction policy
         self.eviction_policy = get_eviction_policy(eviction_policy)
 
@@ -157,6 +227,53 @@ class LoRAMemoryPool:
             or 1
         )
 
+    def _get_num_local_experts(self, base_model: torch.nn.Module) -> int:
+        """Experts owned by this rank. Equals the global count when EP is
+        off, the runner keeps global IDs, or the split isn't even (all
+        three cases fold into `moe_use_local_expert_ids == False`)."""
+        total = self._get_num_experts(base_model)
+        if not self.moe_use_local_expert_ids:
+            return total
+        return total // self.moe_ep_size
+
+    def _global_to_local_expert_id(self, global_eid: int) -> Optional[int]:
+        """Map a global expert id to this rank's local id, or `None` if
+        the expert is not owned by this rank. Pass-through when buffers
+        are globally-keyed."""
+        if not self.moe_use_local_expert_ids:
+            return global_eid
+        local = global_eid - self.moe_ep_rank * self._num_experts_local
+        return local if 0 <= local < self._num_experts_local else None
+
+    def _iter_local_expert_weights(
+        self,
+        weights: Union[torch.Tensor, Dict[int, torch.Tensor]],
+    ) -> Iterator[Tuple[int, torch.Tensor]]:
+        """Yield `(local_expert_id, weight)` pairs for per-expert MoE LoRA
+        inputs, filtered/remapped to this rank's slice. Accepts either a
+        `{global_eid: 2D tensor}` dict or a 3D `[num_experts, *, *]` tensor."""
+        if isinstance(weights, dict):
+            for gid, w in weights.items():
+                lid = self._global_to_local_expert_id(gid)
+                if lid is not None:
+                    yield lid, w
+            return
+
+        if isinstance(weights, torch.Tensor) and weights.dim() == 3:
+            total = weights.shape[0]
+            if self.moe_use_local_expert_ids:
+                start = self.moe_ep_rank * self._num_experts_local
+                count = max(0, min(self._num_experts_local, total - start))
+            else:
+                start, count = 0, total
+            for i in range(count):
+                yield i, weights[start + i]
+            return
+
+        raise TypeError(
+            f"Expected dict or 3D torch.Tensor, got {type(weights).__name__}."
+        )
+
     def _get_standard_shape(
         self,
         module_name: str,
@@ -191,16 +308,19 @@ class LoRAMemoryPool:
             module_name, self.base_hf_config, base_model, layer_idx
         )
         c = get_stacked_multiply(module_name)
+        # MoE modules shard along `moe_tp_size`, not the outer `tp_size`.
+        effective_tp_size = (
+            self.moe_tp_size if self.is_moe_module(module_name) else self.tp_size
+        )
         if (
-            self.tp_size > 1
+            effective_tp_size > 1
             and module_name in ROW_PARALLELISM_LINEAR_LORA_NAMES
             and module_name not in REPLICATED_LINEAR_LORA_NAMES
         ):
-            input_dim = divide(input_dim, self.tp_size)
+            input_dim = divide(input_dim, effective_tp_size)
 
         if self.is_moe_module(module_name):
-            num_experts = self._get_num_experts(base_model)
-            expert_dim = num_experts
+            expert_dim = self._get_num_local_experts(base_model)
             if self.experts_shared_outer_loras and module_name == "gate_up_proj_moe":
                 expert_dim = 1
             return (
@@ -247,17 +367,20 @@ class LoRAMemoryPool:
         _, output_dim = get_hidden_dim(
             module_name, self.base_hf_config, base_model, layer_idx
         )
+        # MoE modules shard along `moe_tp_size`, not the outer `tp_size`.
+        effective_tp_size = (
+            self.moe_tp_size if self.is_moe_module(module_name) else self.tp_size
+        )
         if (
-            self.tp_size > 1
+            effective_tp_size > 1
             and module_name not in ROW_PARALLELISM_LINEAR_LORA_NAMES
             and module_name not in REPLICATED_LINEAR_LORA_NAMES
         ):
-            output_dim = divide(output_dim, self.tp_size)
+            output_dim = divide(output_dim, effective_tp_size)
 
         # Check if MoE module and return appropriate shape
         if self.is_moe_module(module_name):
-            num_experts = self._get_num_experts(base_model)
-            expert_dim = num_experts
+            expert_dim = self._get_num_local_experts(base_model)
             if self.experts_shared_outer_loras and module_name == "down_proj_moe":
                 expert_dim = 1
             return (self.max_loras_per_batch, expert_dim, output_dim, max_lora_dim)
@@ -290,6 +413,10 @@ class LoRAMemoryPool:
     def init_buffers(self, base_model: torch.nn.Module):
         device = next(base_model.parameters()).device
 
+        # Cached once so the per-expert load path doesn't re-walk the HF
+        # config for every adapter.
+        self._num_experts_local: int = self._get_num_local_experts(base_model)
+
         def init_buffer(
             buffer: Dict[str, List[torch.Tensor]],
             target_modules: Set[str],
@@ -717,50 +844,31 @@ class LoRAMemoryPool:
                                         ci * lora_rank : (ci + 1) * lora_rank, :
                                     ],
                                 )
-                    elif isinstance(weights, torch.Tensor) and weights.dim() == 3:
-                        for eid in range(weights.shape[0]):
-                            # Place each component at max_rank-spaced positions
-                            # and zero gaps so the MoE kernel (which processes
-                            # the full max_rank) sees correct data.
-                            target_buffer[buffer_id, eid].zero_()
-                            expert_weight = weights[eid]
-                            if expert_weight is not None:
-                                for ci in range(c):
-                                    buffer_view = target_buffer[
-                                        buffer_id,
-                                        eid,
-                                        ci * max_r : ci * max_r + lora_rank,
-                                        :,
-                                    ]
-                                    load_lora_weight_tensor(
-                                        buffer_view,
-                                        expert_weight[
-                                            ci * lora_rank : (ci + 1) * lora_rank, :
-                                        ],
-                                    )
-                    elif isinstance(weights, dict):
-                        if weights is not None:
-                            for expert_id, expert_weight in weights.items():
-                                # Place each component at max_rank-spaced positions
-                                # and zero gaps so the MoE kernel (which processes
-                                # the full max_rank) sees correct data.
-                                target_buffer[buffer_id, expert_id].zero_()
-                                if expert_weight is not None:
-                                    for ci in range(c):
-                                        buffer_view = target_buffer[
-                                            buffer_id,
-                                            expert_id,
-                                            ci * max_r : ci * max_r + lora_rank,
-                                            :,
-                                        ]
-                                        load_lora_weight_tensor(
-                                            buffer_view,
-                                            expert_weight[
-                                                ci * lora_rank : (ci + 1) * lora_rank, :
-                                            ],
-                                        )
-                        else:
-                            target_buffer[buffer_id].zero_()
+                    elif isinstance(weights, (torch.Tensor, dict)):
+                        # Zero first so any local-expert slot the adapter
+                        # doesn't fill (e.g. out-of-rank under EP) is clean;
+                        # then load owned slots at max_rank-spaced offsets so
+                        # the MoE kernel's [:max_r] / [max_r:2*max_r] slicing
+                        # is correct.
+                        target_buffer[buffer_id].zero_()
+                        for local_eid, expert_weight in self._iter_local_expert_weights(
+                            weights
+                        ):
+                            if expert_weight is None:
+                                continue
+                            for ci in range(c):
+                                buffer_view = target_buffer[
+                                    buffer_id,
+                                    local_eid,
+                                    ci * max_r : ci * max_r + lora_rank,
+                                    :,
+                                ]
+                                load_lora_weight_tensor(
+                                    buffer_view,
+                                    expert_weight[
+                                        ci * lora_rank : (ci + 1) * lora_rank, :
+                                    ],
+                                )
                 else:
                     buffer_view = target_buffer[buffer_id, : lora_rank * c, :]
                     load_lora_weight_tensor(buffer_view, weights)
@@ -807,26 +915,18 @@ class LoRAMemoryPool:
                                 f"type={type(weights)}, "
                                 f"shape={weights.shape if isinstance(weights, torch.Tensor) else 'N/A'}"
                             )
-                    elif isinstance(weights, torch.Tensor) and weights.dim() == 3:
-                        for eid in range(weights.shape[0]):
-                            buffer_view = target_buffer[buffer_id, eid, :, :lora_rank]
-                            w = weights[eid]
+                    elif isinstance(weights, (torch.Tensor, dict)):
+                        # Zero out slots this rank owns but the adapter
+                        # doesn't fill (padded-out / out-of-rank experts);
+                        # then scale+load the ones it does.
+                        target_buffer[buffer_id].zero_()
+                        for local_eid, w in self._iter_local_expert_weights(weights):
                             if w is not None:
                                 w = w * lora_adapter.scaling
-                            load_lora_weight_tensor(buffer_view, w)
-                            # Zero beyond loaded rank — MoE kernel reads full max_rank
-                            target_buffer[buffer_id, eid, :, lora_rank:].zero_()
-                    elif isinstance(weights, dict):
-                        for expert_id, expert_weight in weights.items():
                             buffer_view = target_buffer[
-                                buffer_id, expert_id, :, :lora_rank
+                                buffer_id, local_eid, :, :lora_rank
                             ]
-                            w = expert_weight
-                            if w is not None:
-                                w = w * lora_adapter.scaling
                             load_lora_weight_tensor(buffer_view, w)
-                            # Zero beyond loaded rank — MoE kernel reads full max_rank
-                            target_buffer[buffer_id, expert_id, :, lora_rank:].zero_()
                 else:
                     buffer_view = target_buffer[buffer_id, :, :lora_rank]
                     load_lora_weight_tensor(buffer_view, weights)
diff --git a/test/registered/unit/lora/test_mem_pool_ep_unit.py b/test/registered/unit/lora/test_mem_pool_ep_unit.py
new file mode 100644
index 000000000..b37248c30
--- /dev/null
+++ b/test/registered/unit/lora/test_mem_pool_ep_unit.py
@@ -0,0 +1,611 @@
+"""Unit tests for LoRAMemoryPool's MoE expert-parallel (EP) handling.
+
+Covers the global->local expert-id remapping and per-rank buffer sizing
+introduced so that per-expert MoE LoRA buffers stay aligned with the
+Triton MoE runner's local-id dispatch under `--ep > 1`.
+
+The tests exercise the class behavior without standing up a full server
+or distributed groups: `LoRAMemoryPool` is instantiated via `__new__`
+and only the fields the helpers read are populated. This keeps the
+tests hermetic (CPU-only, no CUDA, no MoE EP group).
+
+Usage:
+    python -m pytest test/registered/unit/lora/test_mem_pool_ep_unit.py -v
+"""
+
+from sglang.test.ci.ci_register import register_cuda_ci
+
+# CPU-only unit test; no CUDA/distributed dependencies.
+register_cuda_ci(est_time=4, suite="stage-b-test-1-gpu-small")
+
+import types
+import unittest
+import unittest.mock as mock
+
+import torch
+
+from sglang.srt.lora.mem_pool import (
+    LoRAMemoryPool,
+    _get_moe_ep_context,
+    _get_moe_tp_context,
+    _moe_runner_keeps_global_expert_ids,
+)
+
+
+def _make_pool(
+    *,
+    num_experts_global: int,
+    moe_ep_size: int,
+    moe_ep_rank: int,
+    moe_use_local_expert_ids: bool,
+) -> LoRAMemoryPool:
+    """Construct a minimal LoRAMemoryPool for helper-level tests.
+
+    Bypasses `__init__` (which requires a real base model, HF config, and
+    device allocations) and sets only the fields consulted by the EP
+    helpers under test.
+    """
+    pool = LoRAMemoryPool.__new__(LoRAMemoryPool)
+    pool.moe_ep_size = moe_ep_size
+    pool.moe_ep_rank = moe_ep_rank
+    pool.moe_use_local_expert_ids = moe_use_local_expert_ids
+    # Helpers under test in this module don't consult moe_tp_size, but set
+    # defaults so accidental reads don't AttributeError.
+    pool.moe_tp_size = 1
+    pool.moe_tp_rank = 0
+    if moe_use_local_expert_ids and num_experts_global % moe_ep_size == 0:
+        pool._num_experts_local = num_experts_global // moe_ep_size
+    else:
+        pool._num_experts_local = num_experts_global
+    return pool
+
+
+def _make_fake_base_model(num_experts: int) -> torch.nn.Module:
+    """Return a `torch.nn.Module` whose `.config` exposes `num_experts`.
+
+    Used by `_get_num_experts` / `_get_num_local_experts` which walk the
+    HF config object. No real weights needed.
+    """
+    model = torch.nn.Linear(4, 4, bias=False)
+    cfg = types.SimpleNamespace(num_experts=num_experts)
+    model.config = cfg
+    return model
+
+
+class TestNumExpertHelpers(unittest.TestCase):
+    """`_get_num_experts` / `_get_num_local_experts` / buffer-dim picker."""
+
+    def test_num_experts_read_from_config(self):
+        model = _make_fake_base_model(num_experts=8)
+        self.assertEqual(LoRAMemoryPool._get_num_experts(model), 8)
+
+    def test_num_local_experts_no_ep(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=1,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=False,
+        )
+        model = _make_fake_base_model(num_experts=8)
+        self.assertEqual(pool._get_num_local_experts(model), 8)
+
+    def test_num_local_experts_with_ep(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=2,
+            moe_use_local_expert_ids=True,
+        )
+        model = _make_fake_base_model(num_experts=8)
+        self.assertEqual(pool._get_num_local_experts(model), 2)
+
+    def test_num_local_experts_with_ep_but_backend_keeps_global_ids(self):
+        """FlashInfer-style backends keep global topk_ids, so even under EP
+        the LoRA buffers must remain globally-keyed.
+        """
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=2,
+            moe_use_local_expert_ids=False,
+        )
+        model = _make_fake_base_model(num_experts=8)
+        self.assertEqual(pool._get_num_local_experts(model), 8)
+
+    def test_uneven_split_disables_local_mapping(self):
+        """Shouldn't happen in practice (base MoE requires even split), but
+        `__init__` must fold uneven splits into `moe_use_local_expert_ids ==
+        False` so `_get_num_local_experts` returns the global count and no
+        remapping happens anywhere downstream.
+        """
+        # Simulate what `LoRAMemoryPool.__init__` would set for an uneven
+        # split: the divisibility guard there forces the flag to False.
+        pool = _make_pool(
+            num_experts_global=7,
+            moe_ep_size=4,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=False,
+        )
+        model = _make_fake_base_model(num_experts=7)
+        self.assertEqual(pool._get_num_local_experts(model), 7)
+
+
+class TestGlobalToLocalExpertId(unittest.TestCase):
+    """`_global_to_local_expert_id` — the per-rank filter + remap."""
+
+    def test_passthrough_without_ep(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=1,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=False,
+        )
+        for gid in range(8):
+            self.assertEqual(pool._global_to_local_expert_id(gid), gid)
+
+    def test_rank0_of_ep4_owns_first_quarter(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=True,
+        )
+        # Owned: 0, 1 -> local 0, 1
+        self.assertEqual(pool._global_to_local_expert_id(0), 0)
+        self.assertEqual(pool._global_to_local_expert_id(1), 1)
+        # Not owned by rank 0.
+        for gid in (2, 3, 4, 5, 6, 7):
+            self.assertIsNone(pool._global_to_local_expert_id(gid))
+
+    def test_rank2_of_ep4_owns_third_quarter(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=2,
+            moe_use_local_expert_ids=True,
+        )
+        # Owned globals 4, 5 -> local 0, 1
+        self.assertEqual(pool._global_to_local_expert_id(4), 0)
+        self.assertEqual(pool._global_to_local_expert_id(5), 1)
+        for gid in (0, 1, 2, 3, 6, 7):
+            self.assertIsNone(pool._global_to_local_expert_id(gid))
+
+    def test_last_rank_owns_last_slice(self):
+        pool = _make_pool(
+            num_experts_global=128,
+            moe_ep_size=4,
+            moe_ep_rank=3,
+            moe_use_local_expert_ids=True,
+        )
+        # Local 0 <-> global 96, local 31 <-> global 127.
+        self.assertEqual(pool._global_to_local_expert_id(96), 0)
+        self.assertEqual(pool._global_to_local_expert_id(127), 31)
+        self.assertIsNone(pool._global_to_local_expert_id(95))
+        self.assertIsNone(pool._global_to_local_expert_id(128))
+
+
+class TestIterLocalExpertWeightsDict(unittest.TestCase):
+    """`_iter_local_expert_weights` with dict input (the common case)."""
+
+    def test_passthrough_without_ep(self):
+        pool = _make_pool(
+            num_experts_global=4,
+            moe_ep_size=1,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=False,
+        )
+        weights = {gid: torch.full((2,), float(gid)) for gid in range(4)}
+        got = {lid: w.tolist() for lid, w in pool._iter_local_expert_weights(weights)}
+        self.assertEqual(
+            got, {0: [0.0, 0.0], 1: [1.0, 1.0], 2: [2.0, 2.0], 3: [3.0, 3.0]}
+        )
+
+    def test_rank0_of_ep4_filters_and_remaps(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=True,
+        )
+        weights = {gid: torch.full((2,), float(gid)) for gid in range(8)}
+        got = {lid: w.tolist() for lid, w in pool._iter_local_expert_weights(weights)}
+        # Rank 0 sees globals 0,1 remapped to locals 0,1.
+        self.assertEqual(got, {0: [0.0, 0.0], 1: [1.0, 1.0]})
+
+    def test_rank3_of_ep4_filters_and_remaps(self):
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=3,
+            moe_use_local_expert_ids=True,
+        )
+        weights = {gid: torch.full((2,), float(gid)) for gid in range(8)}
+        got = {lid: w.tolist() for lid, w in pool._iter_local_expert_weights(weights)}
+        # Rank 3 sees globals 6,7 remapped to locals 0,1.
+        self.assertEqual(got, {0: [6.0, 6.0], 1: [7.0, 7.0]})
+
+    def test_sparse_dict_only_yields_owned_experts(self):
+        """Adapters may only target a subset of experts. The iterator must
+        still correctly filter and remap whatever subset is provided.
+        """
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=2,
+            moe_use_local_expert_ids=True,
+        )
+        # Only globals 1, 4, 5, 7 present in adapter.
+        weights = {
+            1: torch.full((2,), 1.0),
+            4: torch.full((2,), 4.0),
+            5: torch.full((2,), 5.0),
+            7: torch.full((2,), 7.0),
+        }
+        # Rank 2 owns globals 4, 5 -> locals 0, 1.
+        got = {lid: w.tolist() for lid, w in pool._iter_local_expert_weights(weights)}
+        self.assertEqual(got, {0: [4.0, 4.0], 1: [5.0, 5.0]})
+
+    def test_no_experts_owned_yields_nothing(self):
+        """Rank with no matching experts in a sparse dict yields nothing,
+        leaves buffer zeroed.
+        """
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=True,
+        )
+        # Only globals 4, 5 present (owned by rank 2).
+        weights = {4: torch.full((2,), 4.0), 5: torch.full((2,), 5.0)}
+        got = list(pool._iter_local_expert_weights(weights))
+        self.assertEqual(got, [])
+
+
+class TestIterLocalExpertWeightsTensor(unittest.TestCase):
+    """`_iter_local_expert_weights` with 3D tensor input (shared-outer and
+    packed MoE-LoRA formats)."""
+
+    def test_passthrough_without_ep(self):
+        pool = _make_pool(
+            num_experts_global=4,
+            moe_ep_size=1,
+            moe_ep_rank=0,
+            moe_use_local_expert_ids=False,
+        )
+        # [num_experts, rank, hidden] with values carrying the expert id.
+        weights = torch.arange(4 * 2 * 3, dtype=torch.float32).reshape(4, 2, 3)
+        got = [(lid, w.clone()) for lid, w in pool._iter_local_expert_weights(weights)]
+        self.assertEqual([lid for lid, _ in got], [0, 1, 2, 3])
+        for lid, w in got:
+            self.assertTrue(torch.equal(w, weights[lid]))
+
+    def test_rank1_of_ep2_sees_upper_half(self):
+        pool = _make_pool(
+            num_experts_global=4,
+            moe_ep_size=2,
+            moe_ep_rank=1,
+            moe_use_local_expert_ids=True,
+        )
+        weights = torch.arange(4 * 2 * 3, dtype=torch.float32).reshape(4, 2, 3)
+        got = [(lid, w.clone()) for lid, w in pool._iter_local_expert_weights(weights)]
+        # Rank 1 of EP=2 with 4 experts owns globals 2, 3 -> locals 0, 1.
+        self.assertEqual([lid for lid, _ in got], [0, 1])
+        self.assertTrue(torch.equal(got[0][1], weights[2]))
+        self.assertTrue(torch.equal(got[1][1], weights[3]))
+
+    def test_rank_with_partial_tensor_coverage(self):
+        """Defensive: tensor has fewer experts than the expected local slice
+        (e.g. sparse adapter).
+        """
+        pool = _make_pool(
+            num_experts_global=8,
+            moe_ep_size=4,
+            moe_ep_rank=3,
+            moe_use_local_expert_ids=True,
+        )
+        # Only 6 experts present in the tensor; rank 3 expects global 6,7.
+        # So it should still yield local 0 mapped to global 6; global 7 is
+        # beyond the tensor length and must be skipped safely.
+        weights = torch.arange(6 * 2, dtype=torch.float32).reshape(6, 2)
+        # Note: this is 2D, not 3D -> should raise (sanity check).
+        with self.assertRaises(TypeError):
+            list(pool._iter_local_expert_weights(weights))
+
+
+class TestModuleLevelHelpers(unittest.TestCase):
+    """`_get_moe_ep_context` / `_moe_runner_keeps_global_expert_ids`
+    must degrade gracefully when the MoE EP group or runner backend is
+    not yet initialized (e.g. in pure-TP launches or hermetic tests)."""
+
+    def test_ep_context_defaults_when_group_uninitialized(self):
+        # Real process here: the MoE EP group isn't set up in a unit test.
+        # The helper must return (1, 0) rather than raising.
+        ep_size, ep_rank = _get_moe_ep_context()
+        self.assertEqual(ep_size, 1)
+        self.assertEqual(ep_rank, 0)
+
+    def test_tp_context_defaults_when_group_uninitialized(self):
+        # Mirror of `_get_moe_ep_context` for the MoE TP group: if it isn't
+        # initialized (hermetic tests, pure-TP launches), fall back to (1, 0).
+        tp_size, tp_rank = _get_moe_tp_context()
+        self.assertEqual(tp_size, 1)
+        self.assertEqual(tp_rank, 0)
+
+    def test_keeps_global_expert_ids_defaults_to_false(self):
+        # Without a specific flashinfer backend selected, default is False.
+        self.assertFalse(_moe_runner_keeps_global_expert_ids())
+
+
+class TestPoolInitPicksUpEpContext(unittest.TestCase):
+    """`LoRAMemoryPool.__init__` should read EP context from the module-
+    level helpers and set `moe_use_local_expert_ids` correctly."""
+
+    def _new_pool_with_ep(
+        self,
+        ep_size: int,
+        ep_rank: int,
+        keeps_global: bool,
+        num_experts: int = 8,
+        moe_tp_size: int = 1,
+        moe_tp_rank: int = 0,
+        tp_size: int = 1,
+        tp_rank: int = 0,
+    ) -> LoRAMemoryPool:
+        """Construct a pool with `__init__` called, but stop before
+        `init_buffers` — we only care about the EP-context state.
+        """
+        with (
+            mock.patch(
+                "sglang.srt.lora.mem_pool._get_moe_ep_context",
+                return_value=(ep_size, ep_rank),
+            ),
+            mock.patch(
+                "sglang.srt.lora.mem_pool._get_moe_tp_context",
+                return_value=(moe_tp_size, moe_tp_rank),
+            ),
+            mock.patch(
+                "sglang.srt.lora.mem_pool._moe_runner_keeps_global_expert_ids",
+                return_value=keeps_global,
+            ),
+            mock.patch.object(LoRAMemoryPool, "init_buffers", lambda self, _m: None),
+        ):
+            hf_cfg = types.SimpleNamespace(
+                num_hidden_layers=1,
+                hidden_size=8,
+                vocab_size=32,
+                num_experts=num_experts,
+            )
+            base_model = torch.nn.Linear(8, 8, bias=False)
+            base_model.config = hf_cfg
+            return LoRAMemoryPool(
+                base_hf_config=hf_cfg,
+                max_loras_per_batch=1,
+                dtype=torch.bfloat16,
+                tp_size=tp_size,
+                tp_rank=tp_rank,
+                max_lora_rank=8,
+                target_modules={"qkv_proj"},
+                base_model=base_model,
+                eviction_policy="lru",
+                lora_added_tokens_size=0,
+            )
+
+    def test_no_ep(self):
+        pool = self._new_pool_with_ep(ep_size=1, ep_rank=0, keeps_global=False)
+        self.assertEqual(pool.moe_ep_size, 1)
+        self.assertEqual(pool.moe_ep_rank, 0)
+        self.assertFalse(pool.moe_use_local_expert_ids)
+
+    def test_ep4_triton_backend(self):
+        pool = self._new_pool_with_ep(ep_size=4, ep_rank=2, keeps_global=False)
+        self.assertEqual(pool.moe_ep_size, 4)
+        self.assertEqual(pool.moe_ep_rank, 2)
+        self.assertTrue(pool.moe_use_local_expert_ids)
+
+    def test_ep4_flashinfer_cutlass_keeps_global(self):
+        """FlashInfer CUTLASS keeps global topk_ids, so LoRA buffers stay
+        globally-keyed even under EP.
+        """
+        pool = self._new_pool_with_ep(ep_size=4, ep_rank=2, keeps_global=True)
+        self.assertEqual(pool.moe_ep_size, 4)
+        self.assertEqual(pool.moe_ep_rank, 2)
+        self.assertFalse(pool.moe_use_local_expert_ids)
+
+    def test_ep_with_uneven_split_falls_back_to_global_ids(self):
+        """If `num_experts % ep_size != 0` (shouldn't happen in practice,
+        base MoE requires even split) `__init__` must fall back to
+        globally-keyed buffers rather than silently truncating the local
+        slice — otherwise non-zero ranks drop every LoRA weight.
+        """
+        pool = self._new_pool_with_ep(
+            ep_size=4, ep_rank=1, keeps_global=False, num_experts=7
+        )
+        self.assertEqual(pool.moe_ep_size, 4)
+        self.assertEqual(pool.moe_ep_rank, 1)
+        self.assertFalse(pool.moe_use_local_expert_ids)
+
+    def test_init_captures_moe_tp_context(self):
+        """`__init__` must capture moe_tp_size/rank so per-expert MoE LoRA
+        buffers can be sharded by the MoE-TP group (not the outer attn TP).
+        Under `--tp N --ep N` the MoE TP group degenerates to size 1.
+        """
+        pool = self._new_pool_with_ep(
+            ep_size=4,
+            ep_rank=0,
+            keeps_global=False,
+            tp_size=4,
+            tp_rank=0,
+            moe_tp_size=1,
+            moe_tp_rank=0,
+        )
+        self.assertEqual(pool.tp_size, 4)
+        self.assertEqual(pool.moe_tp_size, 1)
+        self.assertEqual(pool.moe_tp_rank, 0)
+
+
+def _fake_base_model_with_hidden_dim(num_experts: int) -> torch.nn.Module:
+    """Fake base model that implements `get_hidden_dim` for MoE + attention
+    modules. Matches the signatures `LoRAMemoryPool.get_lora_{A,B}_shape`
+    call through `sglang.srt.lora.utils.get_hidden_dim`.
+    """
+
+    class _Model(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = torch.nn.Linear(4, 4, bias=False)
+            self.config = types.SimpleNamespace(
+                num_hidden_layers=1,
+                hidden_size=64,
+                num_attention_heads=8,
+                num_key_value_heads=8,
+                head_dim=8,
+                intermediate_size=256,
+                moe_intermediate_size=192,
+                vocab_size=32,
+                num_experts=num_experts,
+            )
+
+        def get_hidden_dim(self, module_name: str, layer_idx: int):
+            cfg = self.config
+            if module_name == "qkv_proj":
+                head = cfg.head_dim
+                return cfg.hidden_size, head * (
+                    cfg.num_attention_heads + cfg.num_key_value_heads * 2
+                )
+            if module_name == "o_proj":
+                return cfg.head_dim * cfg.num_attention_heads, cfg.hidden_size
+            if module_name == "gate_up_proj_moe":
+                return cfg.hidden_size, cfg.moe_intermediate_size * 2
+            if module_name == "down_proj_moe":
+                return cfg.moe_intermediate_size, cfg.hidden_size
+            raise NotImplementedError(module_name)
+
+    return _Model()
+
+
+class TestMoeBufferShardsByMoeTp(unittest.TestCase):
+    """Regression: per-expert MoE LoRA buffers must shard by `moe_tp_size`,
+    not the outer attention `tp_size`.
+
+    Under `--tp N --ep N` (e.g. tp=4, ep=4) `moe_tp_size == 1`, so per-
+    expert weights span the full MoE intermediate dim on every rank; the
+    corresponding LoRA buffer must match. Before the fix, the buffer was
+    divided by `tp_size` (= 4) while `FusedMoEWithLoRA.slice_moe_lora_*`
+    kept the weight full-width, producing a 4x shape-mismatch assert at
+    load time. Non-MoE modules still shard by the outer `tp_size`.
+    """
+
+    def _pool(
+        self,
+        *,
+        tp_size: int,
+        moe_tp_size: int,
+        num_experts: int = 128,
+        ep_size: int = 1,
+        ep_rank: int = 0,
+    ) -> LoRAMemoryPool:
+        pool = LoRAMemoryPool.__new__(LoRAMemoryPool)
+        pool.max_loras_per_batch = 2
+        pool.tp_size = tp_size
+        pool.tp_rank = 0
+        pool.moe_ep_size = ep_size
+        pool.moe_ep_rank = ep_rank
+        pool.moe_tp_size = moe_tp_size
+        pool.moe_tp_rank = 0
+        pool.moe_use_local_expert_ids = ep_size > 1
+        pool._num_experts_local = (
+            num_experts // ep_size if pool.moe_use_local_expert_ids else num_experts
+        )
+        pool.experts_shared_outer_loras = False
+        pool.base_hf_config = types.SimpleNamespace(
+            hidden_size=64,
+            num_attention_heads=8,
+            num_key_value_heads=8,
+            head_dim=8,
+            intermediate_size=256,
+            moe_intermediate_size=192,
+        )
+        return pool
+
+    def test_moe_down_proj_uses_moe_tp_not_attn_tp(self):
+        """down_proj_moe is row-parallel: LoRA-A input_dim = moe_inter must
+        be divided by `moe_tp_size`, NOT `tp_size`. This is the exact shape
+        that failed at load time on `--tp 4 --ep 4` before the fix.
+        """
+        pool = self._pool(tp_size=4, moe_tp_size=1, num_experts=128, ep_size=4)
+        model = _fake_base_model_with_hidden_dim(num_experts=128)
+        num_local = 128 // 4  # 32
+        # A: input_dim = moe_inter / moe_tp_size = 192 / 1 = 192 (pre-fix: 48).
+        self.assertEqual(
+            pool.get_lora_A_shape("down_proj_moe", model, 8, 0),
+            (2, num_local, 8, 192),
+        )
+        # B: output_dim = hidden_size, not row-parallel -> unsharded.
+        self.assertEqual(
+            pool.get_lora_B_shape("down_proj_moe", model, 8, 0),
+            (2, num_local, 64, 8),
+        )
+
+    def test_moe_gate_up_proj_uses_moe_tp_not_attn_tp(self):
+        """gate_up_proj_moe is column-parallel: LoRA-B output_dim =
+        moe_inter*2 must be divided by `moe_tp_size`, not `tp_size`.
+        """
+        pool = self._pool(tp_size=4, moe_tp_size=1, num_experts=128, ep_size=4)
+        model = _fake_base_model_with_hidden_dim(num_experts=128)
+        num_local = 128 // 4
+        # A: input_dim = hidden_size, not row-parallel -> unsharded. Rank
+        # dim is `max_lora_dim * stacked_multiply` (2 for gate_up).
+        self.assertEqual(
+            pool.get_lora_A_shape("gate_up_proj_moe", model, 8, 0),
+            (2, num_local, 16, 64),
+        )
+        # B: output_dim = moe_inter*2 / moe_tp_size = 384 / 1 = 384 (pre-fix: 96).
+        self.assertEqual(
+            pool.get_lora_B_shape("gate_up_proj_moe", model, 8, 0),
+            (2, num_local, 384, 8),
+        )
+
+    def test_moe_tp_gt1_still_shards_moe_dims(self):
+        """Under `--tp 8 --ep 4` the MoE TP group has size 2, so per-expert
+        weights ARE sharded along the MoE inner dim — the LoRA buffer must
+        follow.
+        """
+        pool = self._pool(tp_size=8, moe_tp_size=2, num_experts=128, ep_size=4)
+        model = _fake_base_model_with_hidden_dim(num_experts=128)
+        num_local = 128 // 4
+        # 192 / 2 = 96
+        self.assertEqual(
+            pool.get_lora_A_shape("down_proj_moe", model, 8, 0),
+            (2, num_local, 8, 96),
+        )
+        # 384 / 2 = 192 (B: moe_inter*2 / moe_tp_size).
+        self.assertEqual(
+            pool.get_lora_B_shape("gate_up_proj_moe", model, 8, 0),
+            (2, num_local, 192, 8),
+        )
+        # A: input_dim = hidden_size, unaffected by MoE TP.
+        self.assertEqual(
+            pool.get_lora_A_shape("gate_up_proj_moe", model, 8, 0),
+            (2, num_local, 16, 64),
+        )
+
+    def test_non_moe_modules_unaffected_by_moe_tp(self):
+        """Non-MoE modules must continue to shard by the outer `tp_size`;
+        the MoE-TP substitution applies only to `*_moe` modules.
+        """
+        pool = self._pool(tp_size=4, moe_tp_size=1, num_experts=128, ep_size=4)
+        model = _fake_base_model_with_hidden_dim(num_experts=128)
+        # o_proj is row-parallel: A input_dim sharded by tp_size, B unsharded.
+        o_a = pool.get_lora_A_shape("o_proj", model, 8, 0)
+        o_b = pool.get_lora_B_shape("o_proj", model, 8, 0)
+        # head_dim*num_heads / tp_size = 64 / 4 = 16; B output = hidden_size = 64.
+        self.assertEqual(o_a, (2, 8, 16))
+        self.assertEqual(o_b, (2, 64, 8))
+        # qkv_proj is column-parallel: A unsharded, B sharded by tp_size.
+        q_b = pool.get_lora_B_shape("qkv_proj", model, 8, 0)
+        # head_dim * (heads + 2*kv_heads) / tp_size = 8 * 24 / 4 = 48.
+        self.assertEqual(q_b, (2, 48, 8))
+
+
+if __name__ == "__main__":
+    unittest.main()