diff --git a/vllm_ascend/torchair/torchair_model_runner.py b/vllm_ascend/torchair/torchair_model_runner.py
index d7c55c6e7df..71c67c46681 100644
--- a/vllm_ascend/torchair/torchair_model_runner.py
+++ b/vllm_ascend/torchair/torchair_model_runner.py
@@ -501,13 +501,7 @@ def select_torchair_padded_batch_size(self, batch_size: int):
     def update_torchair_graph_batch_sizes(self):
         # return graph_batch_sizes according to the max number of tokens
         # first pad according to the number of requests
-        if self.is_kv_consumer and self.speculative_config and self.speculative_config.method == 'deepseek_mtp':
-            # pd disaggregation scenario may incorrectly calculate the batch in mtp scenario, so we force set it to max_num_reqs
-            self.torchair_graph_batch_sizes = [self.max_num_reqs]
-            logger.warning(
-                f"is kv_consumer, torch_graph_batch_sizes sets to [max_num_seqs] {[self.max_num_reqs]}"
-            )
-        elif len(self.torchair_graph_batch_sizes) == 0:
+        if len(self.torchair_graph_batch_sizes) == 0:
             self.torchair_graph_batch_sizes = [1, self.max_num_reqs]
         else:
             self.torchair_graph_batch_sizes = sorted(
@@ -537,10 +531,11 @@ def update_torchair_graph_batch_sizes(self):
 
     def _align_graph_size_divisible_by_tp_size(self):
         tp_size = self.parallel_config.tensor_parallel_size
+        lcm_size = math.lcm(tp_size, self.decode_token_per_req)
         new_graph_batch_sizes = []
         for graph_batch_size in self.torchair_graph_batch_sizes:
-            cur_graph_batch_size = self.calculate_new_torchair_graph_batch_size(
-                graph_batch_size, tp_size)
+            cur_graph_batch_size = (graph_batch_size + lcm_size -
+                                    1) // lcm_size * lcm_size
             if cur_graph_batch_size not in new_graph_batch_sizes and \
                 cur_graph_batch_size <= self.scheduler_config.max_num_batched_tokens:
                 new_graph_batch_sizes.append(cur_graph_batch_size)