diff --git a/python/sglang/srt/layers/moe/mega_moe.py b/python/sglang/srt/layers/moe/mega_moe.py
index 4510cf683..c6730a161 100644
--- a/python/sglang/srt/layers/moe/mega_moe.py
+++ b/python/sglang/srt/layers/moe/mega_moe.py
@@ -289,8 +289,8 @@ def _run_mega_routed(
     weight_group_size = getattr(moe.experts, "_mega_moe_weight_group_size", 32)
     deep_gemm.fp8_fp4_mega_moe(
         y,
-        moe.experts.mega_l1_weights,
-        moe.experts.mega_l2_weights,
+        _as_deep_gemm_packed_fp4_weights(moe.experts.mega_l1_weights),
+        _as_deep_gemm_packed_fp4_weights(moe.experts.mega_l2_weights),
         buf,
         recipe=(1, 1, weight_group_size),
         activation="swiglu",
@@ -304,6 +304,17 @@ def _run_mega_routed(
     return y
 
 
+def _as_deep_gemm_packed_fp4_weights(
+    weights: tuple[torch.Tensor, torch.Tensor],
+) -> tuple[torch.Tensor, torch.Tensor]:
+    data, scale = weights
+    if data.dtype is torch.float4_e2m1fn_x2:
+        # DeepGEMM's TVM FFI binding rejects PyTorch's FP4x2 DLPack lanes, while
+        # the MegaMoE kernel expects the same packed bytes exposed as int8.
+        data = data.view(torch.int8)
+    return data, scale
+
+
 def _interleave_mega_moe_gate_up(t: torch.Tensor, gran: int = 8) -> torch.Tensor:
     num_groups, n, *rest = t.shape
     half = n // 2
diff --git a/test/registered/unit/moe/test_glm_megamoe.py b/test/registered/unit/moe/test_glm_megamoe.py
index d9807c182..5df6c0609 100644
--- a/test/registered/unit/moe/test_glm_megamoe.py
+++ b/test/registered/unit/moe/test_glm_megamoe.py
@@ -136,6 +136,25 @@ class TestGLMMegaMoE(unittest.TestCase):
             torch.equal(experts.mega_l1_weights[0], experts.w13_weight.data)
         )
 
+    def test_fp4_weights_use_int8_view_for_deep_gemm_ffi(self):
+        raw = torch.arange(16, dtype=torch.uint8).reshape(2, 8)
+        fp4 = raw.view(torch.float4_e2m1fn_x2)
+        scale = torch.ones((2, 8), dtype=torch.int32)
+
+        data, out_scale = mega_moe._as_deep_gemm_packed_fp4_weights((fp4, scale))
+
+        self.assertEqual(data.dtype, torch.int8)
+        self.assertEqual(data.data_ptr(), fp4.data_ptr())
+        self.assertEqual(data.shape, fp4.shape)
+        self.assertIs(out_scale, scale)
+
+        int8_data = raw.view(torch.int8)
+        same_data, same_scale = mega_moe._as_deep_gemm_packed_fp4_weights(
+            (int8_data, scale)
+        )
+        self.assertIs(same_data, int8_data)
+        self.assertIs(same_scale, scale)
+
 
 if __name__ == "__main__":
     unittest.main()