diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/triton_kernels_moe.py b/python/sglang/srt/layers/moe/fused_moe_triton/triton_kernels_moe.py
index d4be7ae05..33ac80f4f 100644
--- a/python/sglang/srt/layers/moe/fused_moe_triton/triton_kernels_moe.py
+++ b/python/sglang/srt/layers/moe/fused_moe_triton/triton_kernels_moe.py
@@ -13,8 +13,7 @@ from triton_kernels.matmul_ogs import (
     PrecisionConfig,
     matmul_ogs,
 )
-from triton_kernels.numerics import InFlexData, MicroscalingCtx
-from triton_kernels.quantization import downcast_to_mxfp
+from triton_kernels.numerics import InFlexData
 from triton_kernels.routing import GatherIndx, RoutingData, ScatterIndx
 from triton_kernels.swiglu import swiglu_fn
 
@@ -26,30 +25,6 @@ if TYPE_CHECKING:
 def quantize(w, dtype, dev, **opt):
     if dtype == "bf16":
         return w.to(torch.bfloat16), InFlexData()
-    elif dtype == "fp8":
-        wq = w.to(torch.float8_e4m3fn).transpose(-1, -2).contiguous().transpose(-1, -2)
-        return (
-            wq,
-            InFlexData(dtype=wq.dtype, scale=w.abs().max().unsqueeze(0)),
-            MicroscalingCtx(),
-        )
-    else:
-        assert dtype == "mx4", f"{dtype=}"
-        swizzle_mx_scale = opt["swizzle_mx_scale"]
-        swizzle_axis = 2 if swizzle_mx_scale else None
-        w = w.to(torch.bfloat16)
-        w, mx_scales, weight_scale_shape = downcast_to_mxfp(
-            w, torch.uint8, axis=1, swizzle_axis=swizzle_axis
-        )
-        return (
-            w,
-            InFlexData(),
-            MicroscalingCtx(
-                weight_scale=mx_scales,
-                swizzle_mx=swizzle_mx_scale,
-                actual_weight_scale_shape=weight_scale_shape,
-            ),
-        )
 
 
 def triton_kernel_moe_forward(