diff --git a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
index 5b40c27984b..a0ad86f17cd 100644
--- a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
+++ b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
@@ -197,7 +197,7 @@ def convert_llm(model: torch.nn.Module,
                 convert_model: bool=False,
                 save_directory: str=None):
     # whether to set layernorm weight as const
-    layernorm_const = os.environ.get("IPEX_LLM_LAYERNORM_CONST", "1") == "1"
+    layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
     if group_size == 0:
         n_splits_linear = 1
         if qtype == "sym_int8_rtn":
@@ -344,7 +344,7 @@ def convert_llm(model: torch.nn.Module,
                 invalidInputError(False,
                                   "False to InitLLMPipeline.")
     elif model.config.model_type == "qwen2":
-        layernorm_const = os.environ.get("IPEX_LLM_LAYERNORM_CONST", "0") == "1"
+        layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "0") == "1"
         with tempfile.TemporaryDirectory() as temp_dir:
             if save_directory is not None:
                 temp_dir = save_directory
@@ -426,9 +426,11 @@ def convert_llm_for_deploy(model: torch.nn.Module,
     os.mkdir(save_directory)
     weight_dir = os.path.join(save_directory, "model_weights")
     os.mkdir(weight_dir)
+    use_level_zero = os.environ.get("IPEX_LLM_NPU_USE_LEVEL0", "0") == "1"
+    layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
 
     if model.config.model_type == "qwen2":
-        layernorm_const = True
+        layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "0") == "1"
         if model.config.hidden_size == 1536:
             # Qwen2-1.5B-Instruct
             fused_layers = 1
@@ -447,16 +449,28 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                        "weight_num": 7,
                        "weight_idx": 8,
                        "n_splits_linear": n_splits_linear,
-                       "n_splits_down_proj": n_splits_down_proj}
+                       "n_splits_down_proj": n_splits_down_proj,
+                       "use_level_zero": use_level_zero}
         model.config.update(update_dict)
         model.config.save_pretrained(save_directory)
 
         from .qwen import convert_qwen_layer, convert_fused_qwen_layer
         from .qwen import convert_lm_head_and_embedding
-        # save fused_layers blobs of fused decoder layers
-        convert_fused_qwen_layer(model, fused_layers, n_splits_linear, n_splits_down_proj,
-                                 save_directory, weight_dir, transpose_value_cache, kv_len,
-                                 group_size, layernorm_const, "decode")
+        if not use_level_zero:
+            # save fused_layers blobs of fused decoder layers
+            convert_fused_qwen_layer(model, fused_layers, n_splits_linear, n_splits_down_proj,
+                                     save_directory, weight_dir, transpose_value_cache, kv_len,
+                                     group_size, layernorm_const, "decode")
+        else:
+            # save layer_num blobs of each decoder layer
+            layer_num = len(model.model.layers)
+            param_list = []
+            for layer_idx in range(0, layer_num):
+                param_list.append((model, layer_idx, n_splits_linear, n_splits_down_proj,
+                                  save_directory, weight_dir, transpose_value_cache, kv_len,
+                                  group_size, layernorm_const))
+            with Pool() as pool:
+                result = pool.starmap(convert_qwen_layer, param_list)
         # save blob of single prefill layer
         convert_qwen_layer(model, 0, n_splits_linear, n_splits_down_proj,
                            save_directory, weight_dir, transpose_value_cache, max_prompt_len,
@@ -466,7 +480,6 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                                       save_directory, weight_dir,
                                       convert_model=True)
     elif model.config.model_type == "llama":
-        layernorm_const = True
         embedding_post = False
         cos_sin_input = False
         use_prefill_sdp = False
@@ -499,7 +512,8 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                        "embedding_post": embedding_post,
                        "cos_sin_input": cos_sin_input,
                        "n_splits_linear": n_splits_linear,
-                       "n_splits_down_proj": n_splits_down_proj}
+                       "n_splits_down_proj": n_splits_down_proj,
+                       "use_level_zero": use_level_zero}
         model.config.update(update_dict)
         model.config.save_pretrained(save_directory)
 
@@ -519,7 +533,6 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                             save_directory, weight_dir, transpose_value_cache, max_prompt_len,
                             group_size, layernorm_const, "prefill")
     elif model.config.model_type == "minicpm":
-        layernorm_const = True
         fused_layers = 4
         update_dict = {"kv_len": kv_len,
                        "num_head": model.model.layers[0].self_attn.num_heads,
@@ -536,7 +549,8 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                        "model_type": "minicpm",
                        "embedding_post": True,
                        "n_splits_linear": n_splits_linear,
-                       "n_splits_down_proj": n_splits_down_proj}
+                       "n_splits_down_proj": n_splits_down_proj,
+                       "use_level_zero": use_level_zero}
         model.config.update(update_dict)
         model.config.save_pretrained(save_directory)