diff --git a/docs/source/developer_guide/evaluation/profile_execute_duration.md b/docs/source/developer_guide/evaluation/profile_execute_duration.md
index 8989bf9..3b37df6 100644
--- a/docs/source/developer_guide/evaluation/profile_execute_duration.md
+++ b/docs/source/developer_guide/evaluation/profile_execute_duration.md
@@ -9,6 +9,11 @@ The execution duration of each stage (including pre/post-processing, model forwa
 * Use the non-blocking API `ProfileExecuteDuration().capture_async` to set observation points asynchronously when you need to observe the execution duration.
 * Use the blocking API `ProfileExecuteDuration().pop_captured_sync` at an appropriate time to get and print the execution durations of all observed stages.
 
+**We have instrumented the key inference stages (including pre-processing, model forward pass, etc.) for execute duration profiling. Execute the script as follows:**
+```
+VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE=1 python3 vllm-ascend/examples/offline_inference_npu.py
+```
+
 ## Example Output
 
 ```
diff --git a/vllm_ascend/worker/model_runner_v1.py b/vllm_ascend/worker/model_runner_v1.py
index 51ffe1e..9b0a860 100644
--- a/vllm_ascend/worker/model_runner_v1.py
+++ b/vllm_ascend/worker/model_runner_v1.py
@@ -1317,8 +1317,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 for tag, duration in durations.items()
             ]
             captured_name = "Decode" if self.attn_state == AscendAttentionState.DecodeOnly else "Prefill"
-            print(f"Profile execute duration [{captured_name}]:",
-                  " ".join(dr_str))
+            logger.info("Profile execute duration [%s]:%s", captured_name,
+                        " ".join(dr_str))
 
         return model_runner_output