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 ad17631bcc88..bf4c865e09eb 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
@@ -30,12 +30,15 @@
 from sglang.srt.speculative.eagle_info import EagleDraftExtendInput
 from sglang.srt.speculative.spec_utils import fast_topk
 from sglang.srt.utils import (
+    is_hip,
     require_attn_tp_gather,
     require_gathered_buffer,
     require_mlp_sync,
     require_mlp_tp_gather,
 )
 
+_is_hip = is_hip()
+
 if TYPE_CHECKING:
     from sglang.srt.speculative.eagle_worker import EAGLEWorker
 
@@ -401,8 +404,18 @@ def run_once():
                 forward_batch.positions,
                 forward_batch,
             )
-            probs = torch.softmax(ret.next_token_logits, dim=-1)
-            ret.topk_p, ret.topk_index = fast_topk(probs, self.topk, dim=-1)
+            if self.topk == 1 and not _is_hip:
+                # topk=1 → degenerate single-path tree; skip full-vocab softmax
+                # and use argmax(logits) directly. Gated off on ROCm/HIP because
+                # the MTP draft path is sensitive to whether topk_p is the true
+                # probability or a placeholder; see #26358 (revert) / R108.
+                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)
 
             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 eb5c40a7791b..8c6d3bd8a53d 100644
--- a/python/sglang/srt/speculative/eagle_worker_v2.py
+++ b/python/sglang/srt/speculative/eagle_worker_v2.py
@@ -483,8 +483,18 @@ 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}")
-            probs = torch.softmax(logits_output.next_token_logits, dim=-1)
-            topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
+            if self.topk == 1 and not _is_hip:
+                # topk=1 → degenerate single-path tree; skip full-vocab softmax
+                # and use argmax(logits) directly. Gated off on ROCm/HIP because
+                # the MTP draft path is sensitive to whether topk_p is the true
+                # probability or a placeholder; see #26358 (revert) / R108.
+                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)
             maybe_detect_oob(
                 topk_index,
                 0,
@@ -651,8 +661,18 @@ def _draft_extend_for_decode(
             draft_logits_output.hidden_states = draft_logits_output.hidden_states[
                 select_index
             ]
-        probs = torch.softmax(draft_logits_output.next_token_logits, dim=-1)
-        ret_topk_p, ret_topk_index = fast_topk(probs, self.topk, dim=-1)
+        if self.topk == 1 and not _is_hip:
+            # topk=1 → degenerate single-path tree; skip full-vocab softmax
+            # and use argmax(logits) directly. Gated off on ROCm/HIP because
+            # the MTP draft path is sensitive to whether topk_p is the true
+            # probability or a placeholder; see #26358 (revert) / R108.
+            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)
         ret_hidden_states = draft_logits_output.hidden_states
 
         # Construct the return values