diff --git a/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py b/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py
index 5c66a472fa1c..3df46a7aeb4a 100644
--- a/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py
+++ b/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py
@@ -51,10 +51,10 @@
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 
 if _use_aiter:
+    from aiter import ActivationType, QuantType
+    from aiter.fused_moe import fused_moe
     from aiter.ops.shuffle import shuffle_weight
 
-    from sglang.srt.layers.moe.rocm_moe_utils import rocm_fused_experts_tkw1
-
 
 if _is_cuda:
     from sgl_kernel import fused_marlin_moe
@@ -292,7 +292,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module | FusedMoE) -> No
                 max_w13_scales, requires_grad=False
             )
 
-        if _use_aiter:
+        if self.weight_quant.strategy == QuantizationStrategy.CHANNEL and _use_aiter:
             with torch.no_grad():
                 # Pre-shuffle weights
                 layer.w13_weight = torch.nn.Parameter(
@@ -325,23 +325,33 @@ def apply(
 
         moe_runner_config = self.moe_runner_config
 
-        if (
-            _use_aiter
-            and self.weight_quant.strategy == QuantizationStrategy.CHANNEL
-            and moe_runner_config.apply_router_weight_on_input
-        ):
+        if _use_aiter and self.weight_quant.strategy == QuantizationStrategy.CHANNEL:
+            assert not moe_runner_config.no_combine, "unsupported"
             topk_weights, topk_ids, _ = topk_output
-            output = rocm_fused_experts_tkw1(
-                hidden_states=x,
-                w1=layer.w13_weight,
-                w2=layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                activation=moe_runner_config.activation,
-                apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
-                use_fp8_w8a8=True,
-                per_channel_quant=self.weight_quant.strategy
-                == QuantizationStrategy.CHANNEL,
+            if moe_runner_config.apply_router_weight_on_input:
+                assert (
+                    topk_weights.dim() == 2
+                ), "`topk_weights` should be in shape (num_tokens, topk)"
+                _, topk = topk_weights.shape
+                assert (
+                    topk == 1
+                ), "Only support topk=1 when `apply_router_weight_on_input` is True"
+                x = x * topk_weights.to(x.dtype)
+                topk_weights = torch.ones_like(
+                    topk_weights, dtype=torch.float32
+                )  # topk_weights must be FP32 (float32)
+            output = fused_moe(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                topk_weights,
+                topk_ids,
+                activation=(
+                    ActivationType.Silu
+                    if moe_runner_config.activation == "silu"
+                    else ActivationType.Gelu
+                ),
+                quant_type=QuantType.per_Token,
                 w1_scale=layer.w13_weight_scale,
                 w2_scale=layer.w2_weight_scale,
                 a1_scale=layer.w13_input_scale,