diff --git a/python/llm/src/bigdl/llm/optimize.py b/python/llm/src/bigdl/llm/optimize.py
index f1f12283..3169660c 100644
--- a/python/llm/src/bigdl/llm/optimize.py
+++ b/python/llm/src/bigdl/llm/optimize.py
@@ -138,6 +138,12 @@ def load_low_bit(model, model_path):
                       "model should be a instance of "
                       f"`torch.nn.Module`, but got {type(model)} at last.")
     if low_bit:
+        invalidInputError(isinstance(model, torch.nn.Module),
+                          "model should be an instance of `torch.nn.Module`, "
+                          f"but got {type(model)} at last.")
+        invalidInputError(model.device.type in ('cpu', 'meta'),
+                          "Expect model on device `cpu` or `meta`, "
+                          f"but got device type {model.device.type}")
         qtype = ggml_tensor_qtype[low_bit]
         model = ggml_convert_low_bit(model, qtype=qtype, convert_shape_only=True)
 
@@ -173,6 +179,12 @@ def optimize_model(model, low_bit='sym_int4', optimize_llm=True):
     invalidInputError(low_bit in ggml_tensor_qtype,
                       f"Unknown load_in_low_bit value: {low_bit}, expected:"
                       f" sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8.")
+    invalidInputError(isinstance(model, torch.nn.Module),
+                      "model should be an instance of "
+                      f"`torch.nn.Module`, but got {type(model)} at last.")
+    invalidInputError(model.device.type == 'cpu',
+                      "Expect model on device `cpu`, "
+                      f"but got device type {model.device.type}")
     qtype = ggml_tensor_qtype[low_bit]
     model = ggml_convert_low_bit(model, qtype=qtype, optimize_model=optimize_llm)
     # add save_low_bit to pretrained model dynamically