diff --git a/python/sglang/srt/layers/quantization/modelopt_quant.py b/python/sglang/srt/layers/quantization/modelopt_quant.py
index 7647ec30b..ccc6ebffb 100755
--- a/python/sglang/srt/layers/quantization/modelopt_quant.py
+++ b/python/sglang/srt/layers/quantization/modelopt_quant.py
@@ -812,6 +812,11 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         )
         layer.register_parameter("w13_weight_scale", w13_weight_scale)
 
+        # Only use `swizzle_blockscale` for shapes, not for real content
+        layer.w13_blockscale_swizzled = Parameter(
+            self.swizzle_blockscale(layer.w13_weight_scale), requires_grad=False
+        )
+
         w2_weight_scale = ModelWeightParameter(
             data=torch.empty(
                 layer.num_local_experts,
@@ -826,6 +831,10 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         )
         layer.register_parameter("w2_weight_scale", w2_weight_scale)
 
+        layer.w2_blockscale_swizzled = Parameter(
+            self.swizzle_blockscale(layer.w2_weight_scale), requires_grad=False
+        )
+
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
 
         extra_weight_attrs.update(
@@ -1129,16 +1138,12 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
 
             # Process w13 weights
             w13_blockscale_swizzled = self.swizzle_blockscale(layer.w13_weight_scale)
-            layer.w13_blockscale_swizzled = Parameter(
-                w13_blockscale_swizzled, requires_grad=False
-            )
+            layer.w13_blockscale_swizzled.data.copy_(w13_blockscale_swizzled)
             layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
 
             # Process w2 weights
             w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
-            layer.w2_blockscale_swizzled = Parameter(
-                w2_blockscale_swizzled, requires_grad=False
-            )
+            layer.w2_blockscale_swizzled.data.copy_(w2_blockscale_swizzled)
             layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
 
             # Both flashinfer cutlass and regular cutlass use same processing for w2