diff --git a/vllm_ascend/attention/mla_v1.py b/vllm_ascend/attention/mla_v1.py
index 7a704a9b..2db82e9f 100644
--- a/vllm_ascend/attention/mla_v1.py
+++ b/vllm_ascend/attention/mla_v1.py
@@ -1270,6 +1270,7 @@ class AscendMLAImpl(MLAAttentionImpl):
                 AscendAttentionState.SpecDecoding,
                 AscendAttentionState.ChunkedPrefill,
                 AscendAttentionState.DecodeOnly,
+                AscendAttentionState.PrefillNoCache,  # for extremely short prefills
             ]
             and self.speculative_config is not None
         ):
diff --git a/vllm_ascend/worker/model_runner_v1.py b/vllm_ascend/worker/model_runner_v1.py
index 84264698..5fc405dd 100644
--- a/vllm_ascend/worker/model_runner_v1.py
+++ b/vllm_ascend/worker/model_runner_v1.py
@@ -838,14 +838,6 @@ class NPUModelRunner(GPUModelRunner):
     def _build_attn_state(self, num_reqs, num_scheduled_tokens, num_valid_tokens):
         if np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] == 0):
             attn_state = AscendAttentionState.PrefillNoCache
-            # If all prompts are shorter than or equal to decode threshold, they should
-            # be treated as SpecDecoding for correct forward path in mla attention backend
-            if (
-                self.speculative_config
-                and self.speculative_config.method == "mtp"
-                and np.all(num_scheduled_tokens <= self.decode_threshold)
-            ):
-                attn_state = AscendAttentionState.SpecDecoding
         # We assume it is the decode stage, where prefill occurs but only one token is not hit in cache.
         elif np.all(num_scheduled_tokens == 1):
             attn_state = AscendAttentionState.DecodeOnly