diff --git a/python/sglang/srt/models/deepseek_v2.py b/python/sglang/srt/models/deepseek_v2.py
index 12b9b112e..473867493 100644
--- a/python/sglang/srt/models/deepseek_v2.py
+++ b/python/sglang/srt/models/deepseek_v2.py
@@ -447,12 +447,7 @@ class MoEGate(nn.Module):
         if config.topk_method == "noaux_tc" and not is_hash_moe:
             correction_bias_dtype = torch.float32
             if quant_config is not None:
-                if (
-                    quant_config.get_name() == "modelopt_fp4"
-                    and get_moe_runner_backend().is_flashinfer_trtllm()
-                ):
-                    correction_bias_dtype = torch.bfloat16
-                elif _use_aiter and quant_config.get_name() in (
+                if _use_aiter and quant_config.get_name() in (
                     "fp8",
                     "compressed_tensors",
                     "quark",
@@ -511,16 +506,13 @@ class MoEGate(nn.Module):
 
             elif _use_aiter:
                 logits = aiter_dsv3_router_gemm(hidden_states, self.weight)
-            elif _is_npu:
+            elif not _is_cuda:
                 logits = F.linear(hidden_states, self.weight, None)
             else:
-                if self.is_deepseek_v4:
-                    from sglang.jit_kernel.dsv4 import linear_bf16_fp32
+                # cuBLAS bf16 x bf16 -> fp32 GEMM (torch.mm's out_dtype kwarg is CUDA-only)
+                from sglang.jit_kernel.dsv4 import linear_bf16_fp32
 
-                    logits = linear_bf16_fp32(hidden_states, self.weight)
-                else:
-                    # After testing, we may use the faster code in `if deepseek v4` branch
-                    logits = F.linear(hidden_states, self.weight, None)
+                logits = linear_bf16_fp32(hidden_states, self.weight)
 
         return logits