diff --git a/python/sglang/srt/layers/attention/dsv4/compressor.py b/python/sglang/srt/layers/attention/dsv4/compressor.py
index 81cfd1af2cdd..d005a1b355bf 100644
--- a/python/sglang/srt/layers/attention/dsv4/compressor.py
+++ b/python/sglang/srt/layers/attention/dsv4/compressor.py
@@ -25,12 +25,15 @@
 )
 from sglang.srt.mem_cache.deepseek_v4_memory_pool import DeepSeekV4TokenToKVPool
 from sglang.srt.model_executor.forward_context import get_attn_backend
-from sglang.srt.models.deepseek_v2 import _is_hip
+from sglang.srt.models.deepseek_v2 import _is_hip, _use_aiter
 from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import add_prefix, is_npu, set_weight_attrs
 
 _is_npu = is_npu()
 
+if _use_aiter:
+    from aiter.tuned_gemm import tgemm
+
 if TYPE_CHECKING:
     from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
     from sglang.srt.layers.attention.deepseek_v4_backend import DeepseekV4AttnBackend
@@ -454,6 +457,9 @@ def compute_kv_score(self, x: torch.Tensor, forward_batch: ForwardBatch):
     def _compute_wkv_gate(self, x: torch.Tensor) -> torch.Tensor:
         weight = getattr(self.wkv_gate, "weight", None)
         if weight is not None:
+            if _use_aiter and weight.dtype == torch.bfloat16:
+                # aiter's tuned GEMM for these shapes; kv_score is bf16-rounded
+                return tgemm.mm(x, weight, otype=x.dtype).float()
             return linear_bf16_fp32(x, weight)
 
         from sglang.srt.layers.quantization.gguf import fused_mul_mat_gguf