diff --git a/vllm_ascend/ops/moe/token_dispatcher.py b/vllm_ascend/ops/moe/token_dispatcher.py
index 7f74c53fac..ec91e52268 100644
--- a/vllm_ascend/ops/moe/token_dispatcher.py
+++ b/vllm_ascend/ops/moe/token_dispatcher.py
@@ -689,33 +689,16 @@ def _dispatch_postprocess(self, global_input_tokens,
         if self.with_quant:
             assert global_input_tokens_local_experts_indices is not None, \
                 "global_input_tokens_local_experts_indices must be provided"
-            expert_idx_2d = global_input_tokens_local_experts_indices.unsqueeze(
+            dynamic_scale_after_all2all, _ = torch_npu.npu_moe_token_permute(
+                dynamic_scale_after_all2all.unsqueeze(-1),
+                global_input_tokens_local_experts_indices)
+            dynamic_scale_after_all2all = dynamic_scale_after_all2all.squeeze(
                 -1)
-            active_num = global_input_tokens_local_experts_indices.numel()
-
-            if active_num <= 0:
-                reversed_global_input_permutation_mapping = global_input_tokens_local_experts_indices
-                return global_input_tokens, dynamic_scale_after_all2all, reversed_global_input_permutation_mapping
-
-            global_input_tokens, reversed_global_input_permutation_mapping, _, expanded_scale = torch_npu.npu_moe_init_routing_v2(
-                global_input_tokens,
-                expert_idx_2d,
-                scale=dynamic_scale_after_all2all,
-                active_num=active_num,
-                expert_capacity=0,
-                expert_num=self.num_local_experts,
-                expert_tokens_num_type=1,
-                expert_tokens_num_flag=True,
-                active_expert_range=[0, self.num_local_experts],
-                quant_mode=-1,
-                row_idx_type=0,
-            )
-            return global_input_tokens, expanded_scale, reversed_global_input_permutation_mapping
 
         # Non-quantized case
         global_input_tokens, reversed_global_input_permutation_mapping = torch_npu.npu_moe_token_permute(
             global_input_tokens, global_input_tokens_local_experts_indices)
-        return global_input_tokens, None, reversed_global_input_permutation_mapping
+        return global_input_tokens, dynamic_scale_after_all2all, reversed_global_input_permutation_mapping
 
     def _combine_preprocess(self, hidden_states: torch.Tensor,
                             context_metadata: dict) -> torch.Tensor: