diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py
index f2232b1db..eca7f61dc 100644
--- a/vllm/model_executor/layers/quantization/mxfp4.py
+++ b/vllm/model_executor/layers/quantization/mxfp4.py
@@ -1053,32 +1053,32 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 x_scale = x_scale.view(torch.float8_e4m3fn).reshape(*x.shape[:-1], -1)
 
             trtllm_gen_output = trtllm_fp4_block_scale_moe(
-                router_logits.to(torch.bfloat16),
-                None,  # routing_bias
-                x_quant,
-                x_scale,
-                layer.w13_weight,  # uint8 (e2m1 x 2)
-                layer.w13_weight_scale,  # uint8 (e4m3 x 2)
-                layer.w13_bias,  # fp32 per expert per channel
-                layer.gemm1_alpha,  # fp32 per expert
-                layer.gemm1_beta,  # fp32 per expert
-                layer.gemm1_clamp_limit,  # fp32 per expert
-                layer.w2_weight,  # uint8 (e2m1 x 2)
-                layer.w2_weight_scale,  # ue8m0
-                layer.w2_bias,  # fp32 per expert per channel
-                None,  # output1_scale_scalar
-                None,  # output1_scale_gate_scalar
-                None,  # output2_scale_scalar
-                layer.global_num_experts,
-                layer.top_k,
-                None,  # n_group
-                None,  # topk_group
-                self.intermediate_size,  # padded to multiple of 256
-                layer.ep_rank * layer.local_num_experts,  # local_expert_offset
-                self.num_experts,  # local num experts
-                None,  # routed_scaling_factor
-                1 if layer.renormalize else 0,  # routing_method_type, renormalize
-                True,  # do finalize
+                routing_logits=router_logits.to(torch.bfloat16),
+                routing_bias=None,
+                hidden_states=x_quant,
+                hidden_states_scale=x_scale,
+                gemm1_weights=layer.w13_weight,  # uint8 (e2m1 x 2)
+                gemm1_weights_scale=layer.w13_weight_scale,  # uint8 (e4m3 x 2)
+                gemm1_bias=layer.w13_bias,  # fp32 per expert per channel
+                gemm1_alpha=layer.gemm1_alpha,  # fp32 per expert
+                gemm1_beta=layer.gemm1_beta,  # fp32 per expert
+                gemm1_clamp_limit=layer.gemm1_clamp_limit,  # fp32 per expert
+                gemm2_weights=layer.w2_weight,  # uint8 (e2m1 x 2)
+                gemm2_weights_scale=layer.w2_weight_scale,  # ue8m0
+                gemm2_bias=layer.w2_bias,  # fp32 per expert per channel
+                output1_scale_scalar=None,
+                output1_scale_gate_scalar=None,
+                output2_scale_scalar=None,
+                num_experts=layer.global_num_experts,
+                top_k=layer.top_k,
+                n_group=None,
+                topk_group=None,
+                intermediate_size=self.intermediate_size,  # padded to multiple of 256
+                local_expert_offset=layer.ep_rank * layer.local_num_experts,
+                local_num_experts=self.num_experts,
+                routed_scaling_factor=None,
+                routing_method_type=1 if layer.renormalize else 0,
+                do_finalize=True,
                 tune_max_num_tokens=max(self.max_capture_size, 1),
             )[0]
             return trtllm_gen_output