diff --git a/src/axolotl/kernels/moe/hf_triton.py b/src/axolotl/kernels/moe/hf_triton.py
index 08b8a740a..b79c01cb3 100644
--- a/src/axolotl/kernels/moe/hf_triton.py
+++ b/src/axolotl/kernels/moe/hf_triton.py
@@ -79,40 +79,20 @@ def moe_ffn_forward_stub(
     bsz, seqlen, hdim = hidden_states.shape
     flat = hidden_states.view(-1, hdim)
     router_logits = gate_linear(flat)
-    # use hub routing if available; otherwise fallback to softmax+topk
-    routed = None
-    if available():
-        try:
-            routed = route_topk(router_logits, top_k)
-        except Exception:
-            routed = None
-    if routed is None:
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
-        topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
-        topk_weight = topk_weight.to(flat.dtype)
-        x_rep = flat.repeat_interleave(top_k, dim=0)
-        y = torch.empty_like(x_rep)
-        flat_idx = topk_idx.view(-1)
-        for i in range(experts_module.num_experts):
-            expert = experts_module[i]
-            y[flat_idx == i] = expert(x_rep[flat_idx == i])
-        y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
-        return y.reshape(bsz, seqlen, hdim), router_logits
-    # If routed via hub, still fallback to per-expert compute until grouped GEMM path is wired.
-    ex_routing_data, gather_idx, scatter_idx = routed
-    # Convert to naive per-expert compute on packed tokens (future: call matmul_ogs + swiglu)
-    # For now, reconstruct the same result as naive path (no speedup but validates routing).
-    # We map the selected experts from gather_idx back to expert ids via router_logits argmax among top-k.
-    # Simpler: reuse naive computation for correctness; detailed integration will follow.
-    routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
+    # For now, do not call routing to avoid extra overhead until
+    # grouped GEMM integration is complete. Use the naive compute path
+    # for correctness and baseline performance.
+    routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
     topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
-    topk_weight = (topk_weight / topk_weight.sum(dim=-1, keepdim=True)).to(flat.dtype)
+    topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
+    topk_weight = topk_weight.to(flat.dtype)
     x_rep = flat.repeat_interleave(top_k, dim=0)
     y = torch.empty_like(x_rep)
     flat_idx = topk_idx.view(-1)
     for i in range(experts_module.num_experts):
         expert = experts_module[i]
-        y[flat_idx == i] = expert(x_rep[flat_idx == i])
+        sel = flat_idx == i
+        if sel.any():
+            y[sel] = expert(x_rep[sel])
     y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
     return y.reshape(bsz, seqlen, hdim), router_logits