diff --git a/auto_round/modelling/gpt_oss.py b/auto_round/modelling/gpt_oss.py
index 78f73075c..2acae3bb1 100644
--- a/auto_round/modelling/gpt_oss.py
+++ b/auto_round/modelling/gpt_oss.py
@@ -103,20 +103,27 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # Use the original router (it returns scores and indices already softmaxed over top-k)
         router_scores, router_indices = self.router(x)  # scores: [tokens, E], indices: [tokens, k]
 
-        out = self.shared_expert(x) if self.shared_expert is not None else torch.zeros_like(x)
-
-        # Accumulate expert outputs for chosen experts only
-        for j in range(self.top_k):
-            idx = router_indices[:, j]
-            w = router_scores[torch.arange(idx.size(0), device=idx.device), idx].unsqueeze(-1)
-            unique_experts = torch.unique(idx)
-            for e in unique_experts:
-                mask = idx == e
-                out[mask] += self.experts[e](x[mask]) * w[mask]
-
-        out = out.view(B, T, H)
-        router_scores = router_scores.view(B * T, -1)  # shape doesn't matter much; it’s ignored by the decoder
-        return out, router_scores
+        final_hidden_states = self.shared_expert(x) if self.shared_expert is not None else torch.zeros_like(x)
+        num_all_tokens, total_num_experts = x.size(0), self.num_experts
+        mask_weights = torch.zeros((num_all_tokens, total_num_experts), dtype=x.dtype, device=x.device)
+        topk_ids, experts_mask = router_indices, router_scores
+        topk_ids = topk_ids.to(torch.int64)
+
+        mask_weights.scatter_(-1, topk_ids, 1)
+
+        mask_weights = mask_weights[:num_all_tokens, :total_num_experts]
+        mask_weights = mask_weights.transpose(0, 1)
+        experts_mask = experts_mask[:num_all_tokens, :total_num_experts]
+        experts_mask = experts_mask.transpose(0, 1)
+        num_experts = total_num_experts
+        for expert_index in range(num_experts):
+            mask_weight = mask_weights[expert_index].unsqueeze(1)
+            current_state_static = x * mask_weight
+            expert = self.experts[expert_index]
+            expert_output = expert(current_state_static)
+            expert_output = expert_output * experts_mask[expert_index].unsqueeze(1)
+            final_hidden_states += expert_output
+        return final_hidden_states.view(B, T, H), router_scores.view(B * T, -1)
 
 
 def get_replacement_info(config):