diff --git a/tests/unit/test_production_fmha_layer.py b/tests/unit/test_production_fmha_layer.py
index a7c5744a..7bcd3cf0 100644
--- a/tests/unit/test_production_fmha_layer.py
+++ b/tests/unit/test_production_fmha_layer.py
@@ -167,6 +167,23 @@ def main():
                 gate_lin.finalize_weights()
                 router.load_nvfp4_gate(gate_lin)
                 router.load_weights(e_bias=eb.to(dev, torch.float32))
+            else:
+                # BF16 gate weight — quantize to NVFP4
+                gw = all_w.get(f"{mlp_pfx}.gate.weight")
+                if gw is not None:
+                    g_bf16 = gw if gw.shape == (E, H) else gw.T.contiguous()
+                    g_bf16 = g_bf16.bfloat16().to(dev)
+                    g_fp4, g_sf, g_gs = quantize_to_nvfp4(g_bf16)
+                    gate_lin = Nvfp4Linear(in_features=H, out_features=E, device=dev)
+                    gate_lin.fp4 = [g_fp4]
+                    gate_lin.sf = [g_sf]
+                    gate_lin.gs = [g_gs]
+                    gate_lin.ws2 = [torch.tensor([g_gs], device=dev, dtype=torch.float32)]
+                    gate_lin._activation_global_scale = 1.0 / (6.0 * 448.0)
+                    gate_lin._use_runtime_gsa = True
+                    gate_lin.finalize_weights()
+                    router.load_nvfp4_gate(gate_lin)
+                    router.load_weights(e_bias=eb.to(dev, torch.float32))
         router.finalize_weights(); routers[li] = router
 
         moe = Nvfp4MoE(num_experts=cfg["n_routed_experts"], hidden_size=H,