diff --git a/vllm_ascend/quantization/w8a8_dynamic.py b/vllm_ascend/quantization/w8a8_dynamic.py
index 21615f3c7f..abebe0b4c5 100644
--- a/vllm_ascend/quantization/w8a8_dynamic.py
+++ b/vllm_ascend/quantization/w8a8_dynamic.py
@@ -365,29 +365,6 @@ def fused_experts_with_mc2(
         return hidden_states, shared_output
 
 
-def init_routing_quant(hidden_states, top_k, topk_ids, global_num_experts):
-    num_tokens, _ = hidden_states.shape
-    row_idx_len = num_tokens * top_k
-    row_idx = (torch.arange(0,
-                            row_idx_len,
-                            dtype=torch.int32,
-                            device=hidden_states.device).view(
-                                top_k, -1).permute(1, 0).contiguous())
-    hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
-        hidden_states,
-        row_idx=row_idx,
-        expert_idx=topk_ids,
-        active_num=num_tokens)
-
-    expanded_row_idx = (expanded_row_idx.view(top_k, -1).permute(
-        1, 0).contiguous().view(-1))
-    global_expert_tokens = torch.bincount(expanded_expert_idx,
-                                          minlength=global_num_experts)
-    global_expert_tokens = global_expert_tokens.to(torch.int32)
-    quantized_tokens, token_scales = torch_npu.npu_dynamic_quant(hidden_states)
-    return quantized_tokens, expanded_row_idx, global_expert_tokens, token_scales
-
-
 # currently expert parallelism implemented with all2all
 # is under-optimized.
 def fused_experts_with_all2all(
@@ -417,22 +394,19 @@ def fused_experts_with_all2all(
 
     if expert_map is not None:
         global_num_experts = len(expert_map) + global_redundant_expert_num
-        if hasattr(torch_npu, "npu_moe_init_routing_quant"):
-            quantized_tokens, expanded_row_idx, global_expert_tokens, _, token_scales = torch_npu.npu_moe_init_routing_quant(
-                hidden_states,
-                expert_idx=topk_ids.to(torch.int32),
-                active_num=0,
-                expert_capacity=0,
-                expert_num=global_num_experts,
-                drop_pad_mode=0,
-                expert_tokens_num_mode=2,
-                expert_tokens_before_capacity_flag=False,
-                quant_mode=1,
-            )
-        else:
-            quantized_tokens, expanded_row_idx, global_expert_tokens, token_scales = init_routing_quant(
-                hidden_states, top_k, topk_ids, global_num_experts)
-
+        active_num = top_k * num_tokens
+        active_expert_range = [0, global_num_experts]
+        quantized_tokens, expanded_row_idx, global_expert_tokens, token_scales = torch_npu.npu_moe_init_routing_v2(
+            hidden_states,
+            expert_idx=topk_ids.to(torch.int32),
+            active_num=active_num,
+            expert_capacity=0,
+            expert_num=global_num_experts,
+            drop_pad_mode=0,
+            expert_tokens_num_type=1,
+            expert_tokens_num_flag=True,
+            quant_mode=1,
+            active_expert_range=active_expert_range)
         gather_sizes = global_expert_tokens.new_empty(
             global_expert_tokens.shape[0])
         dist.all_to_all_single(gather_sizes, global_expert_tokens)