diff --git a/python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py b/python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py
index 8798086d147b..ad17631bcc88 100644
--- a/python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py
+++ b/python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py
@@ -401,14 +401,8 @@ def run_once():
                 forward_batch.positions,
                 forward_batch,
             )
-            if self.topk == 1:
-                ret.topk_index = torch.argmax(
-                    ret.next_token_logits, dim=-1, keepdim=True
-                )
-                ret.topk_p = torch.ones_like(ret.topk_index, dtype=torch.float32)
-            else:
-                probs = torch.softmax(ret.next_token_logits, dim=-1)
-                ret.topk_p, ret.topk_index = fast_topk(probs, self.topk, dim=-1)
+            probs = torch.softmax(ret.next_token_logits, dim=-1)
+            ret.topk_p, ret.topk_index = fast_topk(probs, self.topk, dim=-1)
 
             forward_batch.out_cache_loc = output_cache_loc_backup
             forward_batch.spec_info.hidden_states = hidden_states_backup
diff --git a/python/sglang/srt/speculative/eagle_worker_v2.py b/python/sglang/srt/speculative/eagle_worker_v2.py
index c864dc12dc76..eb5c40a7791b 100644
--- a/python/sglang/srt/speculative/eagle_worker_v2.py
+++ b/python/sglang/srt/speculative/eagle_worker_v2.py
@@ -483,16 +483,8 @@ def draft_forward(self, forward_batch: ForwardBatch):
                     forward_batch, skip_attn_backend_init=True
                 ).logits_output
             maybe_detect_nan(logits_output.next_token_logits, f"draft_forward step {i}")
-            if self.topk == 1:
-                # topk=1 → degenerate single-path tree; `topk_p` is unused
-                # downstream, so skip softmax and just argmax over logits.
-                topk_index = torch.argmax(
-                    logits_output.next_token_logits, dim=-1, keepdim=True
-                )
-                topk_p = torch.ones_like(topk_index, dtype=torch.float32)
-            else:
-                probs = torch.softmax(logits_output.next_token_logits, dim=-1)
-                topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
+            probs = torch.softmax(logits_output.next_token_logits, dim=-1)
+            topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
             maybe_detect_oob(
                 topk_index,
                 0,
@@ -659,14 +651,8 @@ def _draft_extend_for_decode(
             draft_logits_output.hidden_states = draft_logits_output.hidden_states[
                 select_index
             ]
-        if self.topk == 1:
-            ret_topk_index = torch.argmax(
-                draft_logits_output.next_token_logits, dim=-1, keepdim=True
-            )
-            ret_topk_p = torch.ones_like(ret_topk_index, dtype=torch.float32)
-        else:
-            probs = torch.softmax(draft_logits_output.next_token_logits, dim=-1)
-            ret_topk_p, ret_topk_index = fast_topk(probs, self.topk, dim=-1)
+        probs = torch.softmax(draft_logits_output.next_token_logits, dim=-1)
+        ret_topk_p, ret_topk_index = fast_topk(probs, self.topk, dim=-1)
         ret_hidden_states = draft_logits_output.hidden_states
 
         # Construct the return values