diff --git a/custom_ops/gpu_ops/speculate_decoding/speculate_save_output_with_topk.cc b/custom_ops/gpu_ops/speculate_decoding/speculate_save_output_with_topk.cc
index 4e547d2977..9ef563c62c 100644
--- a/custom_ops/gpu_ops/speculate_decoding/speculate_save_output_with_topk.cc
+++ b/custom_ops/gpu_ops/speculate_decoding/speculate_save_output_with_topk.cc
@@ -54,6 +54,10 @@ void SpeculateSaveOutMmsgTopK(const paddle::Tensor& sampled_token_ids,
   if (!save_each_rank && rank_id > 0) {
     return;
   }
+
+  int max_draft_tokens = sampled_token_ids.shape()[1];
+  int bsz = token_num_per_batch.shape()[0];
+
   auto sampled_token_ids_cpu =
       sampled_token_ids.copy_to(paddle::CPUPlace(), false);
   auto logprob_token_ids_cpu =
@@ -128,7 +132,6 @@ void SpeculateSaveOutMmsgTopK(const paddle::Tensor& sampled_token_ids,
   msg_sed.meta[0] = not_need_stop.data<bool>()[0] ? inference_msg_id_from_env
                                                   : -inference_msg_id_from_env;
   msg_sed.meta[1] = message_flag;
-  int bsz = token_num_per_batch.shape()[0];
   msg_sed.meta[2] = bsz;
   int max_num_logprobs = logprob_token_ids.shape()[1];
   for (int i = 0; i < bsz; i++) {
@@ -146,7 +149,7 @@ void SpeculateSaveOutMmsgTopK(const paddle::Tensor& sampled_token_ids,
       auto* cur_scores = &cur_batch_msg_sed->scores[j * (K + 1)];
       for (int k = 0; k < K + 1; k++) {
         if (k == 0) {
-          cur_tokens[k] = (int)sampled_token_ids_data[token_offset + j];
+          cur_tokens[k] = (int)sampled_token_ids_data[i * max_draft_tokens + j];
           cur_scores[k] = logprob_scores_data[(token_offset + j) * (K + 1) + k];
         } else if (k < max_num_logprobs) {
           cur_tokens[k] =
diff --git a/fastdeploy/model_executor/layers/sample/sampler.py b/fastdeploy/model_executor/layers/sample/sampler.py
index 96edebdda5..1cf8d8bc1b 100644
--- a/fastdeploy/model_executor/layers/sample/sampler.py
+++ b/fastdeploy/model_executor/layers/sample/sampler.py
@@ -839,15 +839,15 @@ class SpeculativeSampler(nn.Layer):
                 raw_logprobs = target_logits.clone()
 
         logprobs_tensors = None
-        token_ids = share_inputs["accept_tokens"]
         if num_logprobs is not None:
+            token_ids = share_inputs["accept_tokens"]
             idx = paddle.arange(share_inputs["accept_tokens"].shape[1], dtype="int32")
             mask = idx < share_inputs["accept_num"].unsqueeze(1)
             token_ids = paddle.masked_select(share_inputs["accept_tokens"], mask)
             logprobs_tensors = self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=token_ids)
 
         sampler_output = SamplerOutput(
-            sampled_token_ids=token_ids,
+            sampled_token_ids=share_inputs["accept_tokens"],
             logprobs_tensors=logprobs_tensors,
             token_num_per_batch=share_inputs["accept_num"],
             cu_batch_token_offset=share_inputs["cu_batch_token_offset"],