diff --git a/auto_round/auto_quantizer.py b/auto_round/auto_quantizer.py
index e0a6b03d..8a4952fc 100644
--- a/auto_round/auto_quantizer.py
+++ b/auto_round/auto_quantizer.py
@@ -317,6 +317,13 @@ def convert_model(self, model: nn.Module):
         return model
 
     def _dynamic_import_inference_linear(self, bits, backend):
+        if (not torch.cuda.is_available()) or "qbits" in backend or "cpu" in backend:
+            try:
+                from intel_extension_for_transformers import qbits  # pylint: disable=E0401
+            except Exception as e:
+                raise ImportError("Please install Intel Extension for Transformers via 'pip install "
+                                  "intel-extension-for-transformers' to  inference on X86 CPU")
+            return qlinear_qbits.QuantLinear
         if bits == 4 and self.exllama2_available and "exllamav2" in backend:
             from auto_round_extension.cuda.qliner_exllamav2 import QuantLinear
         else:
@@ -341,9 +348,10 @@ def _replace_by_quant_layers(self, module: nn.Module, layer_configs, backend):
             data_type = config["data_type"]
             if not (bits <= 8 and data_type == "int"):
                 continue
-            QuantLinear = self._dynamic_import_inference_linear(bits, backend)
+
             layer = get_module(module, layer_name)
             device = get_device(layer)
+            QuantLinear = self._dynamic_import_inference_linear(bits, backend)
             if isinstance(layer, nn.Linear):
                 in_features = layer.in_features
                 out_features = layer.out_features
@@ -363,24 +371,13 @@ def _replace_by_quant_layers(self, module: nn.Module, layer_configs, backend):
                 weight_dtype=layer.weight.dtype,
             )
 
-            if new_layer.qweight.device.type == "cpu": # fallback to qbits linear when qweight on cpu device
-                QuantLinear = qlinear_qbits.QuantLinear
-                new_layer = QuantLinear(  # pylint: disable=E1123
-                bits,
-                group_size,
-                in_features,
-                out_features,
-                bias,
-                weight_dtype=layer.weight.dtype,
-            )
-
             new_layer.device = device
             set_module(module, layer_name, new_layer)
 
     def qbits_post_init(self, model):
         dep_check = True
         for layer in model.modules():
-            if isinstance(layer,qlinear_qbits.QuantLinear):
+            if isinstance(layer, qlinear_qbits.QuantLinear):
                 if dep_check:
                     layer.req_check()
                 layer.post_init()
@@ -408,7 +405,7 @@ class StoreAttr(object):
         model = autoround_post_init(model)
         # there are no side-effects after call qbits_post_init when model quant-type not equal to qbits. 
         model = self.qbits_post_init(model)
-        
+
         return model
 
     def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
diff --git a/auto_round_extension/qbits/qlinear_qbits.py b/auto_round_extension/qbits/qlinear_qbits.py
index 758e615a..1c567946 100644
--- a/auto_round_extension/qbits/qlinear_qbits.py
+++ b/auto_round_extension/qbits/qlinear_qbits.py
@@ -20,12 +20,6 @@
 import torch.nn as nn
 from auto_round.utils import convert_dtype_torch2str, logger
 QBITS_AVAILABLE = True
-try:
-    from intel_extension_for_transformers import qbits  # noqa: F401
-except Exception as e:
-    QBITS_AVAILABLE = False
-    logger.warning(
-        "qlinear_qbits should be used with Intel Extension for Transformers.")
 
 BITS_DTYPE_MAPPING = {
     2: "int2_clip",
@@ -62,6 +56,7 @@ def __init__(
         self.maxq = 2**self.bits - 1
         self.weight_dtype = weight_dtype
         self.asym = True
+        self.qbits = None
 
         self.register_buffer(
             "qweight",
@@ -98,6 +93,7 @@ def __init__(
     def req_check(self):
         torch_version = str(torch.__version__)
         if QBITS_AVAILABLE:
+            import intel_extension_for_transformers
             itrex_version = str(intel_extension_for_transformers.__version__)
             version_match_map = {"1.4": "2.2.0+cpu",
                                  "1.4.1": "2.2.0+cpu", "1.4.2": "2.3.0+cpu"}
@@ -111,6 +107,8 @@ def req_check(self):
             exit(1)
 
     def post_init(self):
+        import intel_extension_for_transformers
+        self.qbits = intel_extension_for_transformers.qbits
         assert self.qweight.device.type == "cpu"
         if self.bias is not None:
             self.bias = self.bias.to(dtype=torch.float32)
@@ -142,7 +140,7 @@ def post_init(self):
 
         logger.info(
             f"QBits repack quantized weight: K:{intweight.shape[0]}, N:{intweight.shape[1]}, weight_dtype:{BITS_DTYPE_MAPPING[self.bits]}, scale_dtype:fp32, compute_dtype:fp32, group_size:{self.group_size}")
-        self.qweight = qbits.repack_quantized_weight(intweight.contiguous(), scales.float().contiguous(), zeros.contiguous(), torch.empty(0),
+        self.qweight = self.qbits.repack_quantized_weight(intweight.contiguous(), scales.float().contiguous(), zeros.contiguous(), torch.empty(0),
                                                      # weight_dtype
                                                      BITS_DTYPE_MAPPING[self.bits],
                                                      # scale_dtype
@@ -167,7 +165,7 @@ def forward(self, x: torch.Tensor):
         bias = self.bias if self.bias is not None else torch.empty(
             0, dtype=torch.float)
 
-        qbits.woq_linear(x, self.qweight, bias, outputs,
+        self.qbits.woq_linear(x, self.qweight, bias, outputs,
                          convert_dtype_torch2str(torch.float),  # compute_dtype
                          BITS_DTYPE_MAPPING[self.bits],  # weight_dtype
                          "fp32",  # scale_dtype