diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py
index ad031a29c96..dc9976e20cc 100644
--- a/neural_compressor/model/onnx_model.py
+++ b/neural_compressor/model/onnx_model.py
@@ -65,6 +65,9 @@ def check_large_model(self):
         """Check model > 2GB."""
         init_size = 0
         for init in self._model.graph.initializer:
+            # if initializer has external data location, return True
+            if init.HasField("data_location") and init.data_location == onnx.TensorProto.EXTERNAL:
+                return True
             # if raise error of initializer size > 2GB, return True
             try:
                 init_bytes = init.SerializeToString()
@@ -72,7 +75,7 @@ def check_large_model(self):
             except Exception as e:
                 if "exceeds maximum protobuf size of 2GB" in str(e):
                     return True
-                else:
+                else: # pragma: no cover
                     raise e
             if init_size > MAXIMUM_PROTOBUF:
                 return True
diff --git a/test/model/test_onnx_model.py b/test/model/test_onnx_model.py
index 56532e3c6dd..5de077cb5f8 100644
--- a/test/model/test_onnx_model.py
+++ b/test/model/test_onnx_model.py
@@ -412,7 +412,6 @@ def test_check_large_model(self):
         import onnx
         import torch
         import torch.nn as nn
-
         from neural_compressor.model.onnx_model import ONNXModel
 
         class Net(nn.Module):
@@ -423,13 +422,19 @@ def __init__(self, in_features, out_features):
             def forward(self, x):
                 x = self.fc(x)
                 return x
-
         # model > 2GB
         model = Net(512, 1024 * 1024)
         input = torch.randn(512, requires_grad=True)
         with torch.no_grad():
             torch.onnx.export(model, (input,), "model.onnx", do_constant_folding=True, opset_version=13)
         model = onnx.load("model.onnx")
+        model = ONNXModel(model) # pass ModelProto
+        self.assertTrue(model.check_large_model())
+
+        model = ONNXModel("model.onnx") # pass string
+        self.assertTrue(model.check_large_model())
+
+        model = onnx.load("model.onnx", load_external_data=False) # not load init
         model = ONNXModel(model)
         self.assertTrue(model.check_large_model())
 
@@ -439,9 +444,17 @@ def forward(self, x):
         with torch.no_grad():
             torch.onnx.export(model, (input,), "model.onnx", do_constant_folding=True, opset_version=13)
         model = onnx.load("model.onnx")
-        model = ONNXModel(model)
+        model = ONNXModel(model) # pass ModelProto
+        self.assertFalse(model.check_large_model())
+
+        model = ONNXModel("model.onnx") # pass string
         self.assertFalse(model.check_large_model())
 
+        model = ONNXModel("model.onnx", load_external_data_for_model=False) # not load init
+        self.assertFalse(model.check_large_model())
+
+        
+
 
 if __name__ == "__main__":
     unittest.main()