diff --git a/python/sglang/srt/layers/quantization/mxfp4.py b/python/sglang/srt/layers/quantization/mxfp4.py
index ce3f5d335..15e3f2007 100644
--- a/python/sglang/srt/layers/quantization/mxfp4.py
+++ b/python/sglang/srt/layers/quantization/mxfp4.py
@@ -213,6 +213,16 @@ if _is_hip:
         dynamic_mxfp4_quant = e8m0_shuffle = err
 
 
+def _pad_hopper_mxfp4_scale(scale, k_size):
+    # triton_kernels' HOPPER_SCALE branch (matmul_details/_matmul.py) loads the w
+    # scales unmasked over cdiv(k_size, 128) tiles; drop when that load is masked.
+    mxfp4_block = 32
+    want = round_up(k_size, 128) // mxfp4_block
+    if scale.shape[-1] >= want:
+        return scale
+    return torch.nn.functional.pad(scale, (0, want - scale.shape[-1]), value=_UE8M0_ONE)
+
+
 def _swizzle_mxfp4(quant_tensor, scale, num_warps):
     """weight swizzle for mxfp4 moe, used for OAI mxfp4 kernel"""
     import triton_kernels.matmul_details.opt_flags as opt_flags
@@ -237,6 +247,8 @@ def _swizzle_mxfp4(quant_tensor, scale, num_warps):
             "split_k": 1,
         }
         opt_flags.update_opt_flags_constraints(constraints)
+        k_size = quant_tensor.shape[-1] * 2  # packed e2m1: 2 fp4 values per byte
+        scale = _pad_hopper_mxfp4_scale(scale=scale, k_size=k_size)
     # transpose the tensor so that the quantization axis is on dim1
     quant_tensor = quant_tensor.transpose(-2, -1)
     scale = scale.transpose(-2, -1)