diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py
index 9d09c46245aa..b2e34c286773 100644
--- a/vllm/model_executor/layers/quantization/mxfp4.py
+++ b/vllm/model_executor/layers/quantization/mxfp4.py
@@ -63,7 +63,7 @@ def should_use_flashinfer_mxfp4():
 
 if current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_FUSED_MOE_A16W4:
     import aiter
-    from aiter.fused_moe import fused_topk, moe_sorting
+    from aiter.fused_moe import fused_moe, fused_topk, moe_sorting
     from aiter.ops.shuffle import shuffle_mxfp4_weight, shuffle_mxfp4_scale
     
 class Mxfp4Config(QuantizationConfig):
@@ -690,51 +690,29 @@ def apply(
                 token_num = x.shape[0]
                 BLOCKM = 16 if token_num < 2048 else 32
                 topk_weights, topk_ids = fused_topk(x, router_logits, top_k, True)
-                sorted_ids, sorted_weights, sorted_expert_ids, num_valid_ids, moe_out = moe_sorting(
-                    topk_ids,
-                    topk_weights,
-                    self.num_experts,
-                    x.shape[1],
-                    torch.bfloat16,
-                    BLOCKM
-                )
-                _, n1, k1 = self.w13_weight_aiter_tensor.shape
-                _, k2, n2 = self.w2_weight_aiter_tensor.shape
-                D = n2 if k2 == k1 else n2*2
-                cktile_moe_out1 = torch.empty((token_num, top_k, D), dtype=torch.bfloat16, device=x.device)
-                aiter.moe_cktile2stages_gemm1(
+                return fused_moe(
                     x,
                     self.w13_weight_aiter_tensor,
-                    cktile_moe_out1,
-                    sorted_ids,
-                    sorted_expert_ids,
-                    num_valid_ids,
-                    top_k,
-                    self.intermediate_pad // 64 * 64 * 2,
-                    self.hidden_pad // 128 * 128, # k_pad_zeros
-                    None, # sorted_weights
-                    None,
-                    self.w13_scale_aiter_tensor,
-                    self.w13_bias_aiter_tensor,
-                    BLOCKM, # block_size
-                )
-                aiter.moe_cktile2stages_gemm2(
-                    cktile_moe_out1,
                     self.w2_weight_aiter_tensor,
-                    moe_out,
-                    sorted_ids,
-                    sorted_expert_ids,
-                    num_valid_ids,
-                    top_k,
-                    self.hidden_pad // 64 * 64, # n_pad_zeros
-                    self.intermediate_pad // 128 * 128,
-                    sorted_weights, # sorted_weights
-                    None,
-                    self.w2_scale_aiter_tensor,
-                    layer.w2_bias,
-                    BLOCKM, # block_size
+                    topk_weights,
+                    topk_ids,
+                    expert_mask=None,
+                    activation=aiter.ActivationType.Swiglu,
+                    quant_type=aiter.QuantType.per_1x32,
+                    doweight_stage1=False,
+                    w1_scale=self.w13_scale_aiter_tensor,
+                    w2_scale=self.w2_scale_aiter_tensor,
+                    a1_scale=None,
+                    a2_scale=None,
+                    block_size_M=BLOCKM,
+                    num_local_tokens=None,
+                    moe_sorting_dispatch_policy=0,
+                    dtype=None,
+                    hidden_pad=self.hidden_pad,
+                    intermediate_pad=self.intermediate_pad,
+                    bias1=self.w13_bias_aiter_tensor,
+                    bias2=layer.w2_bias,
                 )
-                return moe_out
                 
             from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (  # noqa: E501
                 triton_kernel_moe_forward)