diff --git a/python/sglang/srt/layers/quantization/fp8_utils.py b/python/sglang/srt/layers/quantization/fp8_utils.py
index 2a87f70d1..ff10f0a56 100644
--- a/python/sglang/srt/layers/quantization/fp8_utils.py
+++ b/python/sglang/srt/layers/quantization/fp8_utils.py
@@ -8,9 +8,12 @@ from sglang.srt.layers.quantization.fp8_kernel import (
     per_token_group_quant_fp8,
     w8a8_block_fp8_matmul,
 )
-from sglang.srt.utils import is_hip
+from sglang.srt.utils import get_bool_env_var, is_hip
 
 is_hip_ = is_hip()
+if is_hip_ and get_bool_env_var("CK_MOE"):
+    from aiter import gemm_a8w8_blockscale
+
 _is_cuda = torch.cuda.is_available() and torch.version.cuda
 if _is_cuda:
     from sgl_kernel import fp8_blockwise_scaled_mm
@@ -78,6 +81,16 @@ def apply_w8a8_block_fp8_linear(
         output = fp8_blockwise_scaled_mm(
             q_input, weight.T, x_scale, weight_scale.T, out_dtype=input.dtype
         )
+    elif is_hip_ and get_bool_env_var("CK_MOE"):
+        q_input, x_scale = per_token_group_quant_fp8(
+            input_2d, block_size[1], column_major_scales=False
+        )
+        output = torch.zeros(
+            [q_input.shape[0], weight.shape[0]],
+            dtype=input.dtype,
+            device=q_input.device,
+        )
+        gemm_a8w8_blockscale(q_input, weight, x_scale, weight_scale, output)
     else:
         q_input, x_scale = per_token_group_quant_fp8(
             input_2d, block_size[1], column_major_scales=False