diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index f0d94bfbcaba..c42966121e61 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -1362,6 +1362,10 @@ def weight_loader(
     def load_weights(
         self, weights: Iterable[tuple[str, torch.Tensor]]
     ) -> Iterable[str]:
+        from vllm.model_executor.model_loader.weight_utils import (
+            remap_expert_weight_name,
+        )
+
         if (expert_mapping := self.expert_mapping) is None:
             raise ValueError(
                 "`self.expert_mapping` must be provided to "
@@ -1372,7 +1376,10 @@ def load_weights(
             for param_name, weight_name, expert_id, shard_id in expert_mapping:
                 if weight_name not in qual_name:
                     continue
-                weight_name = qual_name.replace(weight_name, param_name)
+                # Remap expert weight name (handles base_layer suffix correctly)
+                weight_name = remap_expert_weight_name(
+                    qual_name, weight_name, param_name
+                )
                 param_name = weight_name.removeprefix(f"{self.layer_name}.")
                 param = getattr(self, param_name)
                 success = self.weight_loader(
diff --git a/vllm/model_executor/model_loader/weight_utils.py b/vllm/model_executor/model_loader/weight_utils.py
index 0c5961561a7d..97decc3104a2 100644
--- a/vllm/model_executor/model_loader/weight_utils.py
+++ b/vllm/model_executor/model_loader/weight_utils.py
@@ -1178,3 +1178,38 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> str | None:
 
     # If there were no matches, return the untouched param name
     return name
+
+
+def remap_expert_weight_name(
+    name: str,
+    weight_name: str,
+    param_name: str,
+) -> str:
+    """Remap expert weight names, handling base_layer prefix for LoRA.
+
+    When loading expert weights, this function maps from checkpoint weight
+    names to model parameter names. It handles the special case where
+    LoRA wraps the original layer with a `base_layer` prefix.
+
+    For example:
+        - Input: name="model.layers.0.mlp.experts.0.up_proj.base_layer.weight"
+                 weight_name="experts.0.up_proj."
+                 param_name="experts.w13_"
+        - Output: "model.layers.0.mlp.experts.base_layer.w13_weight"
+
+    Args:
+        name: The full checkpoint weight name.
+        weight_name: The weight name pattern to match (e.g., "experts.0.up_proj.").
+        param_name: The parameter name to substitute (e.g., "experts.w13_").
+
+    Returns:
+        The remapped weight name with proper base_layer handling.
+    """
+    prefix, _, suffix = name.partition(weight_name)
+    middle = param_name
+    base = "base_layer"
+    if suffix.startswith(f"{base}."):
+        param_list = param_name.split(".", 1)
+        param_list.insert(1, base)
+        middle = ".".join(param_list)
+    return prefix + middle + suffix.removeprefix(f"{base}.")
diff --git a/vllm/model_executor/models/afmoe.py b/vllm/model_executor/models/afmoe.py
index f5dfe4306741..8f9359876590 100644
--- a/vllm/model_executor/models/afmoe.py
+++ b/vllm/model_executor/models/afmoe.py
@@ -36,6 +36,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
 from vllm.model_executor.models.llama import LlamaMLP as AfmoeMLP
@@ -533,7 +534,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     # Do not modify `name` since the loop may continue here
                     # Instead, create a new variable
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
 
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
diff --git a/vllm/model_executor/models/arctic.py b/vllm/model_executor/models/arctic.py
index 0200984c0ec8..9f8e19f322e9 100644
--- a/vllm/model_executor/models/arctic.py
+++ b/vllm/model_executor/models/arctic.py
@@ -38,7 +38,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
@@ -609,7 +612,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     for param_name, weight_name, shard_id in expert_params_mapping:
                         if weight_name not in name:
                             continue
-                        name = name.replace(weight_name, param_name)
+                        # Remap expert weight name (handles base_layer suffix correctly)
+                        name = remap_expert_weight_name(name, weight_name, param_name)
                         if is_pp_missing_parameter(name, self):
                             continue
                         param = params_dict[name]
diff --git a/vllm/model_executor/models/bailing_moe.py b/vllm/model_executor/models/bailing_moe.py
index 4bccee752174..313bab6d99bf 100644
--- a/vllm/model_executor/models/bailing_moe.py
+++ b/vllm/model_executor/models/bailing_moe.py
@@ -55,7 +55,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -524,7 +527,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/dbrx.py b/vllm/model_executor/models/dbrx.py
index db4fe61b0d85..d4fc97bc53a9 100644
--- a/vllm/model_executor/models/dbrx.py
+++ b/vllm/model_executor/models/dbrx.py
@@ -31,6 +31,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -411,7 +412,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             for param_name, weight_name in expert_params_mapping:
                 if weight_name not in name:
                     continue
-                name = name.replace(weight_name, param_name)
+                # Remap expert weight name (handles base_layer suffix correctly)
+                name = remap_expert_weight_name(name, weight_name, param_name)
                 if is_pp_missing_parameter(name, self):
                     continue
                 param = params_dict[name]
diff --git a/vllm/model_executor/models/deepseek_eagle.py b/vllm/model_executor/models/deepseek_eagle.py
index 8f6b4a4b021f..af84f1577368 100644
--- a/vllm/model_executor/models/deepseek_eagle.py
+++ b/vllm/model_executor/models/deepseek_eagle.py
@@ -18,6 +18,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.deepseek_v2 import (
     DeepseekV2DecoderLayer,
@@ -155,7 +156,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     param = params_dict[name]
                     weight_loader = param.weight_loader
diff --git a/vllm/model_executor/models/deepseek_mtp.py b/vllm/model_executor/models/deepseek_mtp.py
index c25e8422da15..dcfcdea03c62 100644
--- a/vllm/model_executor/models/deepseek_mtp.py
+++ b/vllm/model_executor/models/deepseek_mtp.py
@@ -22,6 +22,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
@@ -359,7 +360,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                         # Do not modify `name` since the loop may continue here
                         # Instead, create a new variable
-                        name_mapped = chunk_name.replace(weight_name, param_name)
+                        # Remap expert weight name (handles base_layer suffix correctly)
+                        name_mapped = remap_expert_weight_name(
+                            chunk_name, weight_name, param_name
+                        )
 
                         param = params_dict[name_mapped]
                         # We should ask the weight loader to return success or
diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
index b22cdb6d6c80..19b617c33aa8 100644
--- a/vllm/model_executor/models/deepseek_v2.py
+++ b/vllm/model_executor/models/deepseek_v2.py
@@ -72,6 +72,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.platforms import current_platform
@@ -1643,7 +1644,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                         # Do not modify `name` since the loop may continue here
                         # Instead, create a new variable
-                        name_mapped = chunk_name.replace(weight_name, param_name)
+                        # Remap expert weight name (handles base_layer suffix correctly)
+                        name_mapped = remap_expert_weight_name(
+                            chunk_name, weight_name, param_name
+                        )
 
                         if is_pp_missing_parameter(name_mapped, self):
                             continue
diff --git a/vllm/model_executor/models/dots1.py b/vllm/model_executor/models/dots1.py
index 870a37039f15..f9938dae4a93 100644
--- a/vllm/model_executor/models/dots1.py
+++ b/vllm/model_executor/models/dots1.py
@@ -59,6 +59,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -464,7 +465,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/ernie45_moe.py b/vllm/model_executor/models/ernie45_moe.py
index fbbd31a48538..365307a06aed 100644
--- a/vllm/model_executor/models/ernie45_moe.py
+++ b/vllm/model_executor/models/ernie45_moe.py
@@ -60,6 +60,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.config import set_default_rope_theta
@@ -563,7 +564,11 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     # Do not modify `name` since the loop may continue here
                     # Instead, create a new variable
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
+
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
diff --git a/vllm/model_executor/models/ernie45_vl_moe.py b/vllm/model_executor/models/ernie45_vl_moe.py
index 72f9957fc882..0ca92f10877b 100644
--- a/vllm/model_executor/models/ernie45_vl_moe.py
+++ b/vllm/model_executor/models/ernie45_vl_moe.py
@@ -56,6 +56,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.config import set_default_rope_theta
@@ -736,7 +737,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     moe_offset = int(name.split(".")[-3])
                     is_text_expert = moe_offset <= self.config.moe_num_experts[0] - 1
 
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     if is_text_expert:
                         name = name.replace(".experts.", ".text_experts.")
                     else:
diff --git a/vllm/model_executor/models/glm4_moe.py b/vllm/model_executor/models/glm4_moe.py
index 6fb09be7c67f..b1639755496b 100644
--- a/vllm/model_executor/models/glm4_moe.py
+++ b/vllm/model_executor/models/glm4_moe.py
@@ -59,6 +59,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -554,7 +555,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     # Do not modify `name` since the loop may continue here
                     # Instead, create a new variable
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
 
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
diff --git a/vllm/model_executor/models/glm4_moe_mtp.py b/vllm/model_executor/models/glm4_moe_mtp.py
index e34ae6c85a4f..0f3b494474b5 100644
--- a/vllm/model_executor/models/glm4_moe_mtp.py
+++ b/vllm/model_executor/models/glm4_moe_mtp.py
@@ -38,7 +38,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 
 from .glm4_moe import (
@@ -293,7 +296,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     param = params_dict[name]
                     weight_loader = param.weight_loader
diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py
index 0b1064b6343e..5624719a0191 100644
--- a/vllm/model_executor/models/granitemoe.py
+++ b/vllm/model_executor/models/granitemoe.py
@@ -56,6 +56,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.sequence import IntermediateTensors
@@ -401,7 +402,8 @@ def _load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/granitemoehybrid.py b/vllm/model_executor/models/granitemoehybrid.py
index 3434716b8378..c037ff501e8c 100644
--- a/vllm/model_executor/models/granitemoehybrid.py
+++ b/vllm/model_executor/models/granitemoehybrid.py
@@ -28,7 +28,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 
 from .granitemoe import GraniteMoeMoE
@@ -465,7 +468,8 @@ def _load_quant_expert(name, loaded_weight):
                 if weight_name not in name:
                     continue
 
-                name_mapped = name.replace(weight_name, param_name)
+                # Remap expert weight name (handles base_layer suffix correctly)
+                name_mapped = remap_expert_weight_name(name, weight_name, param_name)
 
                 # Skip layers on other devices.
                 if is_pp_missing_parameter(name_mapped, self):
diff --git a/vllm/model_executor/models/grok1.py b/vllm/model_executor/models/grok1.py
index 0a2e5cf39ffd..6b9da1a2a2e8 100644
--- a/vllm/model_executor/models/grok1.py
+++ b/vllm/model_executor/models/grok1.py
@@ -52,6 +52,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -426,7 +427,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/hunyuan_v1.py b/vllm/model_executor/models/hunyuan_v1.py
index 0e82e84c4edb..b1764d7da3b4 100644
--- a/vllm/model_executor/models/hunyuan_v1.py
+++ b/vllm/model_executor/models/hunyuan_v1.py
@@ -63,6 +63,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -848,7 +849,11 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
 
                     # Do not modify `name` since the loop may continue here
                     # Instead, create a new variable
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
+
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
                     param = params_dict[name_mapped]
diff --git a/vllm/model_executor/models/jamba.py b/vllm/model_executor/models/jamba.py
index b2ad12be1e35..03fceb14ae52 100644
--- a/vllm/model_executor/models/jamba.py
+++ b/vllm/model_executor/models/jamba.py
@@ -33,7 +33,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.models.llama import LlamaMLP as JambaMLP
 from vllm.sequence import IntermediateTensors
 
@@ -427,7 +430,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     if is_pp_missing_parameter(name, self):
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     param = params_dict[name]
                     weight_loader = param.weight_loader
                     weight_loader(
diff --git a/vllm/model_executor/models/kimi_linear.py b/vllm/model_executor/models/kimi_linear.py
index 4562b2202c5e..c454e802c839 100644
--- a/vllm/model_executor/models/kimi_linear.py
+++ b/vllm/model_executor/models/kimi_linear.py
@@ -38,6 +38,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.kimi_linear import KimiLinearConfig
@@ -609,7 +610,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
                 ):
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     if is_pp_missing_parameter(name, self):
                         continue
                     param = params_dict[name]
diff --git a/vllm/model_executor/models/kimi_vl.py b/vllm/model_executor/models/kimi_vl.py
index 85267ccda8a9..3112f088df99 100644
--- a/vllm/model_executor/models/kimi_vl.py
+++ b/vllm/model_executor/models/kimi_vl.py
@@ -65,6 +65,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.deepseek_v2 import DeepseekV2Model
 from vllm.model_executor.models.interfaces import SupportsMultiModal, SupportsPP
@@ -528,7 +529,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
                     ) in enumerate(expert_params_mapping):
                         if weight_name not in name:
                             continue
-                        name = name.replace(weight_name, param_name)
+                        # Remap expert weight name (handles base_layer suffix correctly)
+                        name = remap_expert_weight_name(name, weight_name, param_name)
 
                         if is_pp_missing_parameter(name, self):
                             continue
diff --git a/vllm/model_executor/models/lfm2_moe.py b/vllm/model_executor/models/lfm2_moe.py
index 70804e0a843e..6bc35ee722fc 100644
--- a/vllm/model_executor/models/lfm2_moe.py
+++ b/vllm/model_executor/models/lfm2_moe.py
@@ -35,7 +35,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs import Lfm2MoeConfig
 
@@ -536,7 +539,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     if weight_name not in name:
                         continue
 
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/llama4.py b/vllm/model_executor/models/llama4.py
index 7b3da3e10ab8..c7d2d61a2f21 100644
--- a/vllm/model_executor/models/llama4.py
+++ b/vllm/model_executor/models/llama4.py
@@ -46,6 +46,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.interfaces import MixtureOfExperts
 from vllm.model_executor.models.utils import sequence_parallel_chunk
@@ -465,7 +466,7 @@ def load_moe_expert_weights(
                 continue
 
             # Replace the weight name with the parameter name.
-            full_param_name = name.replace(weight_name, param_name)
+            full_param_name = remap_expert_weight_name(name, weight_name, param_name)
 
             # Skip if the current weight corresponds to a parameter that
             # does not exist on the current PP (pipeline parallel) rank.
diff --git a/vllm/model_executor/models/longcat_flash.py b/vllm/model_executor/models/longcat_flash.py
index 774737387639..872118555d38 100644
--- a/vllm/model_executor/models/longcat_flash.py
+++ b/vllm/model_executor/models/longcat_flash.py
@@ -60,7 +60,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.models.deepseek_v2 import DeepseekV2MLAAttention
 from vllm.sequence import IntermediateTensors
 
@@ -676,7 +679,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     if weight_name not in name:
                         continue
                     is_expert_weight = True
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
                     # Skip mtp
                     if ".mtp." in name_mapped:
                         continue
diff --git a/vllm/model_executor/models/mimo_v2_flash.py b/vllm/model_executor/models/mimo_v2_flash.py
index 12b486f001e0..4651921aec02 100644
--- a/vllm/model_executor/models/mimo_v2_flash.py
+++ b/vllm/model_executor/models/mimo_v2_flash.py
@@ -40,6 +40,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.sequence import IntermediateTensors
@@ -555,7 +556,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 if weight_name not in name:
                     continue
 
-                name_rewritten = name.replace(weight_name, param_name)
+                # Remap expert weight name (handles base_layer suffix correctly)
+                name_rewritten = remap_expert_weight_name(name, weight_name, param_name)
 
                 if is_pp_missing_parameter(name_rewritten, self):
                     continue
diff --git a/vllm/model_executor/models/minicpm.py b/vllm/model_executor/models/minicpm.py
index f104018d3aa6..674a517a4de7 100644
--- a/vllm/model_executor/models/minicpm.py
+++ b/vllm/model_executor/models/minicpm.py
@@ -58,7 +58,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
@@ -525,7 +528,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 for param_name, weight_name, expert_id in expert_params_mapping:
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     if is_pp_missing_parameter(name, self):
                         continue
                     param = params_dict[name]
diff --git a/vllm/model_executor/models/minicpm_eagle.py b/vllm/model_executor/models/minicpm_eagle.py
index 9f3587a6d2fa..bded5c1bb7d5 100644
--- a/vllm/model_executor/models/minicpm_eagle.py
+++ b/vllm/model_executor/models/minicpm_eagle.py
@@ -40,7 +40,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsEagle, SupportsLoRA, SupportsPP
@@ -262,7 +265,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 for param_name, weight_name, expert_id in expert_params_mapping:
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     if is_pp_missing_parameter(name, self):
                         continue
                     param = params_dict[name]
diff --git a/vllm/model_executor/models/minimax_m2.py b/vllm/model_executor/models/minimax_m2.py
index 822bf9b5c93a..141d8cdb9ae7 100644
--- a/vllm/model_executor/models/minimax_m2.py
+++ b/vllm/model_executor/models/minimax_m2.py
@@ -56,6 +56,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -449,7 +450,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/minimax_text_01.py b/vllm/model_executor/models/minimax_text_01.py
index 4bfe3c391c26..3d3e507bba94 100644
--- a/vllm/model_executor/models/minimax_text_01.py
+++ b/vllm/model_executor/models/minimax_text_01.py
@@ -45,7 +45,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.sequence import IntermediateTensors
 
@@ -806,7 +809,8 @@ def load_sparse_moe_weight(
                     continue
                 if weight_name not in name:
                     continue
-                name = name.replace(weight_name, param_name)
+                # Remap expert weight name (handles base_layer suffix correctly)
+                name = remap_expert_weight_name(name, weight_name, param_name)
                 if is_pp_missing_parameter(name, self):
                     return
                 param = params_dict[name]
diff --git a/vllm/model_executor/models/mixtral.py b/vllm/model_executor/models/mixtral.py
index e170c530ca29..c9daa3d0481f 100644
--- a/vllm/model_executor/models/mixtral.py
+++ b/vllm/model_executor/models/mixtral.py
@@ -57,6 +57,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -428,7 +429,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                         continue
 
                     is_expert_weight = True
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
 
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name_mapped, self):
diff --git a/vllm/model_executor/models/nemotron_h.py b/vllm/model_executor/models/nemotron_h.py
index 8bc9ce6154d9..f27cd4b539cd 100644
--- a/vllm/model_executor/models/nemotron_h.py
+++ b/vllm/model_executor/models/nemotron_h.py
@@ -56,6 +56,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.interfaces import (
     HasInnerState,
@@ -700,7 +701,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     # Do not modify `name` since the loop may continue here
                     # Instead, create a new variable
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
 
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
diff --git a/vllm/model_executor/models/olmoe.py b/vllm/model_executor/models/olmoe.py
index a5a926151c5c..0e08fef05686 100644
--- a/vllm/model_executor/models/olmoe.py
+++ b/vllm/model_executor/models/olmoe.py
@@ -46,7 +46,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -383,7 +386,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/openpangu.py b/vllm/model_executor/models/openpangu.py
index 662ecef3ac8f..3c2063289873 100644
--- a/vllm/model_executor/models/openpangu.py
+++ b/vllm/model_executor/models/openpangu.py
@@ -62,6 +62,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.interfaces import (
     MixtureOfExperts,
@@ -1129,7 +1130,10 @@ def load_expert_weight(
             if origin_name not in weight_name:
                 continue
             flag_dict["is_expert_weight"] = True
-            weight_name_mapped = weight_name.replace(origin_name, param_name)
+            # Remap expert weight name (handles base_layer suffix correctly)
+            weight_name_mapped = remap_expert_weight_name(
+                weight_name, origin_name, param_name
+            )
             if is_pp_missing_parameter(weight_name_mapped, self):
                 continue
             param = params_dict[weight_name_mapped]
diff --git a/vllm/model_executor/models/openpangu_mtp.py b/vllm/model_executor/models/openpangu_mtp.py
index 436b7f981b1f..249867c4d6f7 100644
--- a/vllm/model_executor/models/openpangu_mtp.py
+++ b/vllm/model_executor/models/openpangu_mtp.py
@@ -34,7 +34,10 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.models.deepseek_mtp import (
     DeepSeekMultiTokenPredictor,
     DeepSeekMultiTokenPredictorLayer,
@@ -201,7 +204,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     param = params_dict[name]
                     weight_loader = param.weight_loader
diff --git a/vllm/model_executor/models/phimoe.py b/vllm/model_executor/models/phimoe.py
index 14f73d0c6458..d67ec5292dea 100644
--- a/vllm/model_executor/models/phimoe.py
+++ b/vllm/model_executor/models/phimoe.py
@@ -51,6 +51,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.sequence import IntermediateTensors
 
@@ -565,7 +566,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/qwen2_moe.py b/vllm/model_executor/models/qwen2_moe.py
index 82837b77e537..e527daaf4da8 100644
--- a/vllm/model_executor/models/qwen2_moe.py
+++ b/vllm/model_executor/models/qwen2_moe.py
@@ -55,7 +55,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -476,7 +479,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
 
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py
index 0be81ecc7dd3..f698a4574add 100644
--- a/vllm/model_executor/models/qwen3_moe.py
+++ b/vllm/model_executor/models/qwen3_moe.py
@@ -61,6 +61,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.sequence import IntermediateTensors
@@ -567,7 +568,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     # Do not modify `name` since the loop may continue here
                     # Instead, create a new variable
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
 
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py
index ccf6cc6e5894..6a626f5a5fd4 100644
--- a/vllm/model_executor/models/qwen3_next.py
+++ b/vllm/model_executor/models/qwen3_next.py
@@ -65,6 +65,7 @@
 )
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
+    remap_expert_weight_name,
     sharded_weight_loader,
 )
 from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
@@ -1083,7 +1084,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/qwen3_next_mtp.py b/vllm/model_executor/models/qwen3_next_mtp.py
index 83694caa5248..8abd82e34665 100644
--- a/vllm/model_executor/models/qwen3_next_mtp.py
+++ b/vllm/model_executor/models/qwen3_next_mtp.py
@@ -18,7 +18,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.model_executor.models.qwen3_next import (
     Qwen3NextDecoderLayer,
     Qwen3NextRMSNorm,
@@ -184,7 +187,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue
diff --git a/vllm/model_executor/models/qwen3_vl_moe.py b/vllm/model_executor/models/qwen3_vl_moe.py
index 3186804488e5..705fcb878d0f 100644
--- a/vllm/model_executor/models/qwen3_vl_moe.py
+++ b/vllm/model_executor/models/qwen3_vl_moe.py
@@ -42,6 +42,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
+    remap_expert_weight_name,
 )
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors
@@ -238,7 +239,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     # Anyway, this is an expert weight and should not be
                     # attempted to load as other weights later
                     is_expert_weight = True
-                    name_mapped = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name_mapped = remap_expert_weight_name(
+                        name, weight_name, param_name
+                    )
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
                     if is_fused_expert:
diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index 7077f1a22e8d..9d3782ed6fc6 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -34,7 +34,10 @@
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    remap_expert_weight_name,
+)
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.step3_vl import Step3TextConfig
 
@@ -498,7 +501,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     param_name, weight_name, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
+                    # Remap expert weight name (handles base_layer suffix correctly)
+                    name = remap_expert_weight_name(name, weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
                         continue