diff --git a/dlinfer/ops/llm.py b/dlinfer/ops/llm.py
index 6434acb..59f207f 100644
--- a/dlinfer/ops/llm.py
+++ b/dlinfer/ops/llm.py
@@ -534,11 +534,12 @@ def weight_quant_matmul(
 @register_custom_op("dlinfer::fused_moe", ["hidden_states"])
 def fused_moe(
     hidden_states: Tensor,
-    top_k: int,
-    topk_ids: Tensor,
-    topk_weights: Tensor,
     gate_up_weights: Tensor,
     down_weights: Tensor,
+    topk_weights: Tensor,
+    topk_ids: Tensor,
+    topk: int,
+    renormalize: bool,
 ) -> Tensor:
     """
     Implement the Fused Mixture of Experts (MoE) model.
@@ -556,7 +557,13 @@ def fused_moe(
 
     """
     return vendor_ops_registry["fused_moe"](
-        hidden_states, top_k, topk_ids, topk_weights, gate_up_weights, down_weights
+        hidden_states,
+        gate_up_weights,
+        down_weights,
+        topk_weights,
+        topk_ids,
+        topk,
+        renormalize
     )
 
 
diff --git a/dlinfer/vendor/ascend/torch_npu_ops.py b/dlinfer/vendor/ascend/torch_npu_ops.py
index 05444df..9a95250 100644
--- a/dlinfer/vendor/ascend/torch_npu_ops.py
+++ b/dlinfer/vendor/ascend/torch_npu_ops.py
@@ -406,11 +406,12 @@ def weight_quant_matmul(
 @register_ops(vendor_ops_registry)
 def fused_moe(
     hidden_states: Tensor,
-    top_k: int,
-    topk_ids: Tensor,
-    topk_weights: Tensor,
     gate_up_weights: Tensor,
     down_weights: Tensor,
+    topk_weights: Tensor,
+    topk_ids: Tensor,
+    top_k: int,
+    renormalize: bool,
 ) -> Tensor:
     seq_length = hidden_states.size(0)
     moe_output = torch.zeros_like(hidden_states)
diff --git a/dlinfer/vendor/maca/maca_ops.py b/dlinfer/vendor/maca/maca_ops.py
index 2838bab..3eb9fb9 100644
--- a/dlinfer/vendor/maca/maca_ops.py
+++ b/dlinfer/vendor/maca/maca_ops.py
@@ -348,31 +348,22 @@ def silu_and_mul(x: Tensor, dim: int = -1) -> Tensor:
 
 @register_ops(vendor_ops_registry)
 def fused_moe(
-    hidden_states: torch.Tensor,
+    hidden_states: Tensor,
+    gate_up_weights: Tensor,
+    down_weights: Tensor,
+    topk_weights: Tensor,
+    topk_ids: Tensor,
     top_k: int,
-    topk_ids: torch.LongTensor,
-    topk_weights: torch.Tensor,
-    gate_up_weights: torch.Tensor,
-    down_weights: torch.Tensor,
-):
-    N, D = hidden_states.shape
-    hidden_states = hidden_states.view(N, -1, D).repeat(1, top_k, 1).reshape(-1, D)
-    out = torch.zeros(
-        N * top_k,
-        down_weights.shape[1],
-        dtype=hidden_states.dtype,
-        device=hidden_states.device,
+    renormalize: bool,
+) -> Tensor:
+    N = hidden_states.size(0)
+    topk_weights = topk_weights.reshape(N, top_k)
+    topk_ids = topk_ids.reshape(N, top_k)
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    return vllm.model_executor.layers.fused_moe.fused_experts(
+        hidden_states, gate_up_weights, down_weights, topk_weights, topk_ids
     )
-    for i in range(gate_up_weights.shape[0]):
-        mask = topk_ids == i
-        if mask.sum():
-            out[mask] = silu_and_mul(
-                hidden_states[mask] @ gate_up_weights[i].transpose(0, 1)
-            ) @ down_weights[i].transpose(0, 1)
-    return (
-        out.view(N, -1, down_weights.shape[1])
-        * topk_weights.view(N, -1, 1).to(out.dtype)
-    ).sum(dim=1)
 
 
 @register_ops(vendor_ops_registry)