diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
index 35f06c6de..2c02a7463 100644
--- a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
+++ b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
@@ -1063,10 +1063,15 @@ class FlashInferFP4MoE(FusedMoE):
             gemm1_weights_scale=self.gemm1_scales_fp4_shuffled.data.view(
                 torch.float8_e4m3fn
             ),
+            gemm1_bias=None,
+            gemm1_alpha=None,
+            gemm1_beta=None,
+            gemm1_clamp_limit=None,
             gemm2_weights=self.gemm2_weights_fp4_shuffled.data,
             gemm2_weights_scale=self.gemm2_scales_fp4_shuffled.data.view(
                 torch.float8_e4m3fn
             ),
+            gemm2_bias=None,
             output1_scale_scalar=self.g1_scale_c.data,
             output1_scale_gate_scalar=self.g1_alphas.data,
             output2_scale_scalar=self.g2_alphas.data,
diff --git a/python/sglang/srt/layers/quantization/modelopt_quant.py b/python/sglang/srt/layers/quantization/modelopt_quant.py
index fca0ee38b..4e2b3a53e 100755
--- a/python/sglang/srt/layers/quantization/modelopt_quant.py
+++ b/python/sglang/srt/layers/quantization/modelopt_quant.py
@@ -764,8 +764,6 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             )
 
         # TODO(ch-wan): check if this is needed
-        layer.num_experts = num_experts
-        layer.num_local_experts = num_experts
         layer.intermediate_size_per_partition = intermediate_size_per_partition
         layer.params_dtype = params_dtype
         layer.quant_config = self.quant_config
@@ -1106,7 +1104,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
                 layer.w13_weight_scale,
             )
 
-            print("Applied flashinfer weight processing for both w13 and w2")
+            logger.info_once("Applied flashinfer weight processing for both w13 and w2")
 
         else:
             # CUTLASS processing - handle w13 and w2 separately
@@ -1126,7 +1124,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
 
             # Both flashinfer cutlass and regular cutlass use same processing for w2
-            print("Applied weight processing for both w13 and w2")
+            logger.info_once("Applied weight processing for both w13 and w2")
 
             # Set up CUTLASS MoE parameters
             device = layer.w13_weight.device