diff --git a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py
index efd726c..d509fcb 100644
--- a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py
+++ b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/layers/sparse_moe_mlp.py
@@ -284,34 +284,28 @@ class SparseMoeMlp(nn.Module):
 
 
     def forward_experts_nofused(self, hidden_states, expert_logits):
-        hidden_states_shape = hidden_states.shape
+        # Dense approach: each expert processes ALL tokens, then mask by routing
+        # weights. This avoids data-dependent control flow (variable-size slicing,
+        # conditional branches, torch.unique, torch.tensor creation) that is
+        # incompatible with MLU graph capture.
+        num_tokens, hidden_size = hidden_states.shape
         topk_values, topk_indices = self.topk_softmax(expert_logits)
-        expand_gather_idx, scatter_idx, expand_token_count, cusum_token_count = self.generate_gather_idx(
-            topk_indices)
-        # no expert is routed, then expand_gather_idx, expand_scatter_idx has no item,
-        # expand_token_count and expand_cusum_token_count has item but the value is all zero
-        # so this rank should only return final_hidden_states with zero value
-        if expand_gather_idx.numel() == 0:
-            final_hidden_states = torch.zeros_like(hidden_states,
-                                                   dtype=hidden_states.dtype,
-                                                   device=hidden_states.device)
-            return final_hidden_states
 
-        expand_hidden_states = self.expand_input(hidden_states, expand_gather_idx)
+        final_hidden_states = torch.zeros(
+            num_tokens, hidden_size,
+            dtype=hidden_states.dtype, device=hidden_states.device)
 
-        expand_output_list = []
-        expand_cusum_token_count = cusum_token_count[self.start_expert_id:self.end_expert_id +
-                                                     1] - cusum_token_count[self.start_expert_id]
-        for expert_idx, num_tokens_per_expert in enumerate(expand_token_count):
-            if num_tokens_per_expert > 0:
-                expert_hidden_states = expand_hidden_states[
-                    expand_cusum_token_count[expert_idx]:expand_cusum_token_count[expert_idx + 1]]
-                expert_output = self.experts[expert_idx](expert_hidden_states)
-                expert_output = expert_output[0] if isinstance(expert_output, (tuple, list)) else expert_output
-                expand_output_list.append(expert_output)
-        expand_output = torch.cat(expand_output_list, dim=0)
-        final_hidden_states = self.combine_moe(expand_output, scatter_idx, cusum_token_count, hidden_states_shape,
-                                               topk_values)
+        for expert_idx in range(self.num_experts_per_rank):
+            global_expert_idx = self.start_expert_id + expert_idx
+            expert_output = self.experts[expert_idx](hidden_states)
+            expert_output = expert_output[0] if isinstance(
+                expert_output, (tuple, list)) else expert_output
+
+            # Routing weight per token for this expert
+            expert_mask = (topk_indices == global_expert_idx).to(topk_values.dtype)
+            expert_weights = (topk_values * expert_mask).sum(dim=-1, keepdim=True)
+
+            final_hidden_states = final_hidden_states + expert_output * expert_weights
 
         return final_hidden_states