[Perf] Fix dsv3_router_gemm heuristic (#44217)

Signed-off-by: LopezCastroRoberto <rocastro@redhat.com>

vllm/model_executor/layers/fused_moe/router/gate_linear.py

@@ -44,11 +44,10 @@ class GateLinear(ReplicatedLinear):
         force_fp32_compute: bool = False,
         prefix: str = "",
     ):
-        is_hopper_or_blackwell = current_platform.is_device_capability(
-            (9, 0)
-        ) or current_platform.is_device_capability_family(100)
+        is_hopper = current_platform.is_device_capability((9, 0))
+        is_blackwell = current_platform.is_device_capability_family(100)
         can_use_specialized_kernels = (
-            current_platform.is_cuda() and is_hopper_or_blackwell and not bias
+            current_platform.is_cuda() and (is_hopper or is_blackwell) and not bias
         )
 
         # If fp32 compute is required and no specialized kernel is available,
@@ -73,13 +72,16 @@ class GateLinear(ReplicatedLinear):
             and output_size in self.DSV3_SUPPORTED_NUM_EXPERTS
             and input_size in self.DSV3_SUPPORTED_HIDDEN_SIZES
         )
+        # See https://github.com/vllm-project/vllm/pull/44217
+        # for more details.
+        self._dsv3_max_batch = 16 if is_hopper else 8
 
         # fp32 specialized kernel eligibility (SM90+, exact dims, fp32 weight)
         self.allow_fp32_router_gemm = (
             not bias
             and self.weight.dtype == torch.float32
             and current_platform.is_cuda()
-            and is_hopper_or_blackwell
+            and (is_hopper or is_blackwell)
             and output_size in self.FP32_SUPPORTED_NUM_EXPERTS
             and input_size in self.FP32_SUPPORTED_HIDDEN_SIZES
         )
@@ -112,7 +114,7 @@ class GateLinear(ReplicatedLinear):
         self, x: torch.Tensor
     ) -> torch.Tensor | tuple[torch.Tensor, Parameter | None]:
         # Tier 1: DSV3 specialized kernel
-        if self.allow_dsv3_router_gemm and x.shape[0] <= 16:
+        if self.allow_dsv3_router_gemm and x.shape[0] <= self._dsv3_max_batch:
             output = ops.dsv3_router_gemm(
                 hidden_states=x,
                 router_weight=self.weight,