diff --git a/dsv4/kernels/router/dense_router_decode.py b/dsv4/kernels/router/dense_router_decode.py
index b9a6f80a..d4ae5813 100644
--- a/dsv4/kernels/router/dense_router_decode.py
+++ b/dsv4/kernels/router/dense_router_decode.py
@@ -18,7 +18,7 @@ import torch
 
 def dense_router_dispatch(
     hidden_states: torch.Tensor,       # [N, hidden_size] BF16
-    W_gate: torch.Tensor,              # [hidden_size, num_experts] BF16
+    W_gate: torch.Tensor,              # [num_experts, hidden_size] BF16 (pre-transposed for F.linear)
     e_bias: torch.Tensor,              # [num_experts] FP32
     routed_scaling_factor: float,
     top_k: int,
@@ -29,8 +29,15 @@ def dense_router_dispatch(
 
     BF16 GEMM via torch.nn.functional.linear (cuBLAS, SM100 tensor cores),
     then fused activation + top-k via the CUDA kernel.
+
+    CUDA-graph-compatible: W_gate must be pre-transposed to [E, H] BF16
+    so no .T or .float() calls happen during capture. The GEMM runs in BF16
+    (Blackwell tensor cores handle BF16 natively). Only the output logits
+    are cast to FP32 for the sqrt(softplus) activation.
     """
-    logits = torch.nn.functional.linear(hidden_states.float(), W_gate.T.float())
+    # BF16 GEMM — W_gate is pre-transposed to [E, H] for F.linear
+    logits_bf16 = torch.nn.functional.linear(hidden_states, W_gate)
+    logits = logits_bf16.float()  # BF16 → FP32 for sqrt(softplus) numerical stability
     from dsv4.kernels.router._activation_topk import run_fused_activation_topk
     run_fused_activation_topk(
         logits, e_bias, routed_scaling_factor, top_k,
diff --git a/dsv4/layers/router.py b/dsv4/layers/router.py
index fbdf3db6..798b3c05 100644
--- a/dsv4/layers/router.py
+++ b/dsv4/layers/router.py
@@ -141,7 +141,8 @@ class Router:
                 f"e_bias shape {tuple(e_bias.shape)} != ({self.num_experts},)"
             self.e_bias = e_bias.to(device=self.device, dtype=torch.float32)
             if W_gate is not None:
-                self.W_gate = W_gate.to(device=self.device, dtype=torch.bfloat16)
+                # Pre-transpose to [E, H] for F.linear — avoids .T during graph capture
+                self.W_gate = W_gate.to(device=self.device, dtype=torch.bfloat16).T.contiguous()
             # gate_lin is set separately via load_nvfp4_gate()
         else:  # hash
             if hash_lut is None: