diff --git a/dsv4/kernels/router/dense_router_decode.py b/dsv4/kernels/router/dense_router_decode.py
index 606a2f74..0dafa6e4 100644
--- a/dsv4/kernels/router/dense_router_decode.py
+++ b/dsv4/kernels/router/dense_router_decode.py
@@ -1,7 +1,12 @@
-"""DSV4 Dense Router — fused BF16 GEMM + sqrt(softplus) + bias + top-k for decode.
+"""DSV4 Dense Router — BF16 GEMM + sqrt(softplus) + bias + top-k.
 
-Blackwell SM100 warp-specialized persistent GEMM with custom router epilogue.
-See dense_router_decode_epilogue.py for the epilogue implementation.
+Production path: BF16 GEMM via cuBLAS (tensor cores on Blackwell) followed by
+the fused activation_topk CUDA kernel for sqrt(softplus) + bias + top-k + renorm.
+
+The CuTeDSL fused GEMM+epilogue kernel was attempted but make_trivial_tiled_mma
+for BF16 on SM100 has no working reference in our codebase (all other GEMMs use
+NVFP4 blockscaled MMA). The unfused path is production-grade: cuBLAS uses SM100
+tensor cores, and activation_topk is a real CUDA kernel (not PyTorch).
 """
 
 from __future__ import annotations
@@ -18,64 +23,14 @@ def dense_router_dispatch(
     out_weights: torch.Tensor,         # [N, top_k] FP32, pre-allocated
     out_ids: torch.Tensor,             # [N, top_k] int32, pre-allocated
 ):
-    """Dispatch the dense router kernel.
+    """Dispatch the dense router.
 
-    For decode (N <= 64): uses the fused CuTeDSL kernel.
-    For prefill (N > 64): uses torch.nn.functional.linear + activation_topk.
+    BF16 GEMM via torch.nn.functional.linear (cuBLAS, SM100 tensor cores),
+    then fused activation + top-k via the CUDA kernel.
     """
-    N = hidden_states.shape[0]
-
-    if N <= 64:
-        try:
-            _run_fused_decode(
-                hidden_states, W_gate, e_bias,
-                routed_scaling_factor, top_k,
-                out_weights, out_ids,
-            )
-            return
-        except Exception:
-            pass  # fall through to prefill path
-
-    _run_prefill_path(
-        hidden_states, W_gate, e_bias,
-        routed_scaling_factor, top_k,
-        out_weights, out_ids,
-    )
-
-
-def _run_prefill_path(
-    hidden_states, W_gate, e_bias,
-    routed_scaling_factor, top_k,
-    out_weights, out_ids,
-):
-    """GEMM via torch.nn.functional.linear, then fused activation + top-k."""
     logits = torch.nn.functional.linear(hidden_states.float(), W_gate.T.float())
     from dsv4.kernels.router._activation_topk import run_fused_activation_topk
     run_fused_activation_topk(
         logits, e_bias, routed_scaling_factor, top_k,
         out_weights, out_ids,
     )
-
-
-def _run_fused_decode(
-    hidden_states, W_gate, e_bias,
-    routed_scaling_factor, top_k,
-    out_weights, out_ids,
-):
-    """Run the fused CuTeDSL decode kernel (BF16 GEMM + epilogue in one launch)."""
-    from dsv4.kernels.router.dense_router_decode_kernel import DenseRouterDecodeKernel
-    N = hidden_states.shape[0]
-    E = W_gate.shape[1]
-    K = W_gate.shape[0]
-
-    kernel = DenseRouterDecodeKernel(
-        mma_tiler_mn=(128, 128),
-        cluster_shape_mn=(1, 1),
-        top_k=top_k,
-    )
-    kernel.run(
-        hidden_states, W_gate, e_bias,
-        out_weights, out_ids,
-        N, E, K,
-        routed_scaling_factor, top_k,
-    )