diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
index 2b59d29f..efc5aaf2 100644
--- a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
+++ b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
@@ -145,8 +145,8 @@ python minicpm.py --disable-transpose-value-cache
 python minicpm.py --repo-id-or-model-path openbmb/MiniCPM-2B-sft-bf16 --disable-transpose-value-cache
 ```
 
-#### High CPU Utilization
-You can reduce CPU utilization by setting the environment variable with `set IPEX_LLM_CPU_LM_HEAD=0`.
+#### Better Performance with High CPU Utilization
+You could enable optimization by setting the environment variable with `set IPEX_LLM_CPU_LM_HEAD=1` for better performance. But this will cause high CPU utilization.
 
 
 ### Sample Output
diff --git a/python/llm/src/ipex_llm/transformers/npu_models/convert_mp.py b/python/llm/src/ipex_llm/transformers/npu_models/convert_mp.py
index a1b07a8c..ba40729a 100644
--- a/python/llm/src/ipex_llm/transformers/npu_models/convert_mp.py
+++ b/python/llm/src/ipex_llm/transformers/npu_models/convert_mp.py
@@ -43,7 +43,8 @@ def optimize_llm_pre(model: torch.nn.Module, qtype):
             model.apply(pre_compute_inv_freq)
 
     # lm_head to cpu optimization
-    if os.environ.get("IPEX_LLM_CPU_LM_HEAD", "1") != "0":
+    if os.environ.get("IPEX_LLM_CPU_LM_HEAD", "0") != "0":
+        # disable the optimization by default
         from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8
         if qtype == "sym_int4_rtn":
             lm_qtype = SYM_INT4