diff --git a/tests/kernels/test_gate_linear_rocm_dispatch.py b/tests/kernels/test_gate_linear_rocm_dispatch.py
new file mode 100644
index 0000000000..865b58e18e
--- /dev/null
+++ b/tests/kernels/test_gate_linear_rocm_dispatch.py
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Platform-agnostic eligibility tests for GateLinear's fused router GEMM.
+
+These assert the ``allow_cublas_router_gemm`` dispatch flag directly, so they
+run device-free by mocking the platform predicates. The flag decides whether
+the bf16xbf16->fp32 router GEMM uses ``torch.mm``'s fused out_dtype epilogue
+(one kernel) or falls back to a bf16 matmul plus a standalone bf16->fp32 copy.
+
+The ROCm branch is guarded on ``not bias`` because ``torch.mm`` has no bias
+term; a biased gate must fall back so the bias is not silently dropped.
+"""
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.router.gate_linear as gate_linear_mod
+from vllm.model_executor.layers.fused_moe.router.gate_linear import GateLinear
+
+
+def _make_gate(
+    monkeypatch,
+    *,
+    is_rocm: bool,
+    is_cuda: bool = False,
+    bias: bool = False,
+    params_dtype: torch.dtype = torch.bfloat16,
+    out_dtype: torch.dtype | None = torch.float32,
+) -> GateLinear:
+    """Build a GateLinear with platform predicates mocked, no GPU needed."""
+    for target in (
+        "vllm.model_executor.layers.linear",
+        "vllm.model_executor.parameter",
+    ):
+        monkeypatch.setattr(
+            f"{target}.get_tensor_model_parallel_rank",
+            lambda: 0,
+        )
+        monkeypatch.setattr(
+            f"{target}.get_tensor_model_parallel_world_size",
+            lambda: 1,
+        )
+
+    platform = gate_linear_mod.current_platform
+    monkeypatch.setattr(platform, "is_cuda", lambda: is_cuda)
+    monkeypatch.setattr(platform, "is_rocm", lambda: is_rocm)
+    # Force the CUDA specialized-kernel gate off so ROCm eligibility is the
+    # only thing under test (these are the SM90/SM100 capability checks).
+    monkeypatch.setattr(platform, "is_device_capability", lambda *a, **k: False)
+    monkeypatch.setattr(platform, "is_device_capability_family", lambda *a, **k: False)
+
+    return GateLinear(
+        input_size=2048,
+        output_size=64,
+        bias=bias,
+        out_dtype=out_dtype,
+        params_dtype=params_dtype,
+    )
+
+
+def test_rocm_no_bias_bf16_fp32_enables_fused_gemm(monkeypatch):
+    gate = _make_gate(monkeypatch, is_rocm=True, bias=False)
+    assert not gate.allow_specialized_router_gemm
+    assert gate.allow_cublas_router_gemm
+
+
+def test_rocm_bias_disables_fused_gemm(monkeypatch):
+    # torch.mm cannot add a bias, so a biased gate must not take the fused path.
+    gate = _make_gate(monkeypatch, is_rocm=True, bias=True)
+    assert not gate.allow_cublas_router_gemm
+
+
+def test_rocm_fp32_weight_disables_fused_gemm(monkeypatch):
+    gate = _make_gate(monkeypatch, is_rocm=True, params_dtype=torch.float32)
+    assert not gate.allow_cublas_router_gemm
+
+
+def test_rocm_non_fp32_out_dtype_disables_fused_gemm(monkeypatch):
+    gate = _make_gate(monkeypatch, is_rocm=True, out_dtype=torch.bfloat16)
+    assert not gate.allow_cublas_router_gemm
+
+
+def test_non_rocm_non_cuda_disables_fused_gemm(monkeypatch):
+    # Neither the CUDA specialized path nor the ROCm branch applies.
+    gate = _make_gate(monkeypatch, is_rocm=False, is_cuda=False)
+    assert not gate.allow_cublas_router_gemm
+
+
+def test_rocm_set_out_dtype_enables_fused_gemm(monkeypatch):
+    gate = _make_gate(monkeypatch, is_rocm=True, bias=False, out_dtype=None)
+    assert not gate.allow_cublas_router_gemm
+    gate.set_out_dtype(torch.float32)
+    assert gate.allow_cublas_router_gemm
+
+
+def test_rocm_set_out_dtype_respects_bias_guard(monkeypatch):
+    gate = _make_gate(monkeypatch, is_rocm=True, bias=True, out_dtype=None)
+    gate.set_out_dtype(torch.float32)
+    assert not gate.allow_cublas_router_gemm
diff --git a/vllm/model_executor/layers/fused_moe/router/gate_linear.py b/vllm/model_executor/layers/fused_moe/router/gate_linear.py
index 39c4f4d815..ba46c1de65 100644
--- a/vllm/model_executor/layers/fused_moe/router/gate_linear.py
+++ b/vllm/model_executor/layers/fused_moe/router/gate_linear.py
@@ -114,9 +114,17 @@ class GateLinear(ReplicatedLinear):
         if self.allow_bf16x3_router_gemm:
             logger.info_once("Enabled experimental SM100 BF16x3 router GEMM.")
 
-        # cuBLAS bf16→fp32 eligibility
+        # Fused bf16 x bf16 -> fp32 GEMM eligibility. torch.mm's out_dtype
+        # epilogue folds the fp32 cast into the GEMM, removing the standalone
+        # bf16->fp32 copy kernel that otherwise runs before grouped_topk.
+        # cuBLAS on CUDA (SM90+, via allow_specialized_router_gemm); hipBLASLt on
+        # ROCm, which supports the same out_dtype epilogue.
+        self._router_gemm_no_bias = not bias
         self.allow_cublas_router_gemm = (
-            self.allow_specialized_router_gemm
+            (
+                self.allow_specialized_router_gemm
+                or (current_platform.is_rocm() and self._router_gemm_no_bias)
+            )
             and self.weight.dtype == torch.bfloat16
             and self.out_dtype == torch.float32
         )
@@ -148,7 +156,10 @@ class GateLinear(ReplicatedLinear):
 
         if (
             not self.allow_cublas_router_gemm
-            and self.allow_specialized_router_gemm
+            and (
+                self.allow_specialized_router_gemm
+                or (current_platform.is_rocm() and self._router_gemm_no_bias)
+            )
             and out_dtype == torch.float32
         ):
             self.allow_cublas_router_gemm = self.weight.dtype == torch.bfloat16