diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py
index 8e49ccea5fd4..567c031938f5 100644
--- a/vllm/model_executor/models/qwen3_moe.py
+++ b/vllm/model_executor/models/qwen3_moe.py
@@ -29,6 +29,7 @@
 from typing import Any
 
 import torch
+import torch.nn.functional as F
 from torch import nn
 
 from vllm.attention.layer import Attention
@@ -42,7 +43,7 @@
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe import SharedFusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -86,6 +87,7 @@ def __init__(
         hidden_act: str,
         quant_config: QuantizationConfig | None = None,
         reduce_results: bool = True,
+        expert_gate: torch.nn.Linear | None = None,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -109,12 +111,17 @@ def __init__(
                 f"Unsupported activation: {hidden_act}. Only silu is supported for now."
             )
         self.act_fn = SiluAndMul()
+        self.expert_gate = expert_gate
 
     def forward(self, x):
         gate_up, _ = self.gate_up_proj(x)
-        x = self.act_fn(gate_up)
-        x, _ = self.down_proj(x)
-        return x
+        out = self.act_fn(gate_up)
+        out, _ = self.down_proj(out)
+
+        if self.expert_gate is not None:
+            out = F.sigmoid(self.expert_gate(x)[0]) * out
+
+        return out
 
 
 class Qwen3MoeSparseMoeBlock(nn.Module):
@@ -159,12 +166,46 @@ def __init__(
             self.physical_expert_start + self.n_local_physical_experts
         )
 
-        self.experts = FusedMoE(
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate",
+        )
+
+        shared_expert_intermediate_size = getattr(
+            config, "shared_expert_intermediate_size", 0
+        )
+        if shared_expert_intermediate_size > 0:
+            self.shared_expert_gate = ReplicatedLinear(
+                config.hidden_size,
+                1,
+                bias=False,
+                quant_config=None,
+                prefix=f"{prefix}.shared_expert_gate",
+            )
+            self.shared_expert = Qwen3MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=shared_expert_intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                expert_gate=self.shared_expert_gate,
+                prefix=f"{prefix}.shared_expert",
+            )
+        else:
+            self.shared_expert_gate = None
+            self.shared_expert = None
+
+        self.experts = SharedFusedMoE(
+            shared_experts=self.shared_expert,
+            gate=self.gate,
             num_experts=self.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=True,
+            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -173,14 +214,6 @@ def __init__(
             is_sequence_parallel=self.is_sequence_parallel,
         )
 
-        self.gate = ReplicatedLinear(
-            config.hidden_size,
-            config.num_experts,
-            bias=False,
-            quant_config=quant_config,
-            prefix=f"{prefix}.gate",
-        )
-
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         assert hidden_states.dim() <= 2, (
             "Qwen3MoeSparseMoeBlock only supports 1D or 2D inputs"
@@ -194,15 +227,22 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
-        final_hidden_states = self.experts(
+        shared_out, fused_out = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
+        final_hidden_states = (
+            shared_out + fused_out if shared_out is not None else fused_out
+        )
 
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
+        elif self.tp_size > 1:
+            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
+                final_hidden_states
+            )
 
         # return to 1d if input is 1d
         return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states
@@ -467,7 +507,7 @@ def forward(
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
+        return SharedFusedMoE.make_expert_params_mapping(
             self,
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",