diff --git a/python/llm/src/ipex_llm/optimize.py b/python/llm/src/ipex_llm/optimize.py
index 2af7ccc5e4a..0bf1c41023a 100644
--- a/python/llm/src/ipex_llm/optimize.py
+++ b/python/llm/src/ipex_llm/optimize.py
@@ -254,7 +254,9 @@ def optimize_model(model, low_bit='sym_int4', optimize_llm=True, modules_to_not_
                                  torch_dtype=torch_dtype,
                                  optimize_model=optimize_llm,
                                  modules_to_not_convert=modules_to_not_convert,
-                                 cpu_embedding=cpu_embedding)
+                                 cpu_embedding=cpu_embedding,
+                                 disable_optimize_pre=kwargs.pop("disable_optimize_pre",
+                                                                 False))
     # add save_low_bit to pretrained model dynamically
     import types
     model._bigdl_config = dict()
diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index f18eac4b08f..169b7102ff4 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -1081,7 +1081,8 @@ def ggml_convert_low_bit(model, qtype, optimize_model=True,
                          torch_dtype="auto",
                          imatrix_data=None,
                          embedding_qtype=None,
-                         mixed_precision=False):
+                         mixed_precision=False,
+                         disable_optimize_pre=False):
     if qtype in ggml_tensor_qtype.values():
         index = list(ggml_tensor_qtype.values()).index(qtype)
         logger.info(f"Converting the current model to "
@@ -1104,7 +1105,7 @@ def ggml_convert_low_bit(model, qtype, optimize_model=True,
         model = _optimize_ipex(model, qtype)
         return model
 
-    if optimize_model:
+    if optimize_model and not disable_optimize_pre:
         model = _optimize_pre(model, qtype)
 
     act_order = False
diff --git a/python/llm/src/ipex_llm/transformers/low_bit_linear.py b/python/llm/src/ipex_llm/transformers/low_bit_linear.py
index f9b0757842d..28e4c0834ae 100644
--- a/python/llm/src/ipex_llm/transformers/low_bit_linear.py
+++ b/python/llm/src/ipex_llm/transformers/low_bit_linear.py
@@ -764,6 +764,7 @@ def __init__(self, input_features, output_features, bias=True,
         # weigh_type = 3 means weight has been transposed by esimd method
         self.weight_type = 1
         self.optimize_lm_head = optimize_lm_head
+        self.disable_fp16_opt = False
 
     def forward(self, x: torch.Tensor):
         # only work for GPU
@@ -779,8 +780,11 @@ def forward(self, x: torch.Tensor):
             self.weight.data = self.weight.data.to(x.dtype)
 
         if not self.use_esimd_kernel(x):
-            if get_ipex_version() < "2.1.10+xpu" \
-                    or get_xpu_device_type(x) not in ["arc", "flex", "pvc"]:
+            if (
+                get_ipex_version() < "2.1.10+xpu"
+                or get_xpu_device_type(x) not in ["arc", "flex", "pvc"]
+                or self.disable_fp16_opt
+            ):
                 if self.weight_type == 2:
                     self.weight = torch.nn.Parameter(self.weight.transpose(0, 1).contiguous(),
                                                      requires_grad=False)
@@ -845,6 +849,8 @@ def forward(self, x: torch.Tensor):
 
     def use_esimd_kernel(self, x):
         gpu_type = get_xpu_device_type(x)
+        if self.disable_fp16_opt:
+            return False
         # esimd kernel can only be used for Arc and Flex
         if gpu_type not in ["arc", "flex"]:
             return False
diff --git a/python/llm/src/ipex_llm/transformers/model.py b/python/llm/src/ipex_llm/transformers/model.py
index 323b73cbce9..7411afde974 100644
--- a/python/llm/src/ipex_llm/transformers/model.py
+++ b/python/llm/src/ipex_llm/transformers/model.py
@@ -445,6 +445,7 @@ def load_convert(cls, q_k, optimize_model, *args, **kwargs):
         mixed_precision = kwargs.pop("mixed_precision", False)
         if embedding_qtype is not None:
             embedding_qtype = ggml_tensor_qtype[embedding_qtype]
+        disable_optimize_pre = kwargs.pop("disable_optimize_pre", False)
         _args = copy.deepcopy(args)
         _kwargs = copy.deepcopy(kwargs)
         awq_config = None
@@ -513,7 +514,8 @@ def load_convert(cls, q_k, optimize_model, *args, **kwargs):
                                      torch_dtype=kwargs.get("torch_dtype", 'auto'),
                                      imatrix_data=imatrix_data,
                                      embedding_qtype=embedding_qtype,
-                                     mixed_precision=mixed_precision)
+                                     mixed_precision=mixed_precision,
+                                     disable_optimize_pre=disable_optimize_pre)
 
         if disk_embedding:
             from ipex_llm.transformers.embedding import DiskEmbedding