diff --git a/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py b/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
index 30a37a65..a5424840 100644
--- a/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
+++ b/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
@@ -32,14 +32,19 @@ def unpack_ue4m3_u32(x_u32):
     Each uint32 contains 4 UE4M3 values packed in bits [0:8], [8:16], [16:24], [24:32].
     Must use bit reinterpret (view), NOT value cast (to) — byte 0x3F is the float8
     whose bits are 0x3F (~0.984), NOT the integer 63.
+    
+    CUDA doesn't implement bitwise ops on uint32, so we cast to int32 first.
     """
-    x_u32 = x_u32.contiguous()
-    M, N = x_u32.shape
-    # Extract 4 bytes as uint8, then bit-reinterpret to float8_e4m3fn
-    b0 = (x_u32 & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
-    b1 = ((x_u32 >> 8) & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
-    b2 = ((x_u32 >> 16) & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
-    b3 = ((x_u32 >> 24) & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
+    # CUDA uint32 lacks bitwise ops — use int32
+    x_i32 = x_u32.to(torch.int32)
+    M, N = x_i32.shape
+    
+    # Extract 4 bytes, cast to uint8, then bit-reinterpret to float8_e4m3fn
+    b0 = (x_i32 & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
+    b1 = ((x_i32 >> 8) & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
+    b2 = ((x_i32 >> 16) & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
+    b3 = ((x_i32 >> 24) & 0xFF).to(torch.uint8).view(torch.float8_e4m3fn)
+    
     # Interleave into (M, N*4)
     out = torch.empty(M, N * 4, dtype=torch.float8_e4m3fn, device=x_u32.device)
     out[:, 0::4] = b0