diff --git a/quantization/nlp/bert/migraphx/e2e_migraphx_bert_example.py b/quantization/nlp/bert/migraphx/e2e_migraphx_bert_example.py
index cd891ff94..0c423036d 100644
--- a/quantization/nlp/bert/migraphx/e2e_migraphx_bert_example.py
+++ b/quantization/nlp/bert/migraphx/e2e_migraphx_bert_example.py
@@ -277,6 +277,109 @@ def get_op_nodes_not_followed_by_specific_op(model, op1, op2):
 
     return not_selected_op1_nodes
 
+def custom_write_calibration_table(calibration_cache, dir="."):
+    """
+    Helper function to write calibration table to files.
+    """
+
+    import json
+    import logging
+    import flatbuffers
+    import numpy as np
+
+    import onnxruntime.quantization.CalTableFlatBuffers.KeyValue as KeyValue
+    import onnxruntime.quantization.CalTableFlatBuffers.TrtTable as TrtTable
+    from onnxruntime.quantization.calibrate import CalibrationMethod, TensorData, TensorsData
+
+    logging.info(f"calibration cache: {calibration_cache}")
+
+    class MyEncoder(json.JSONEncoder):
+        def default(self, obj):
+            if isinstance(obj, (TensorData, TensorsData)):
+                return obj.to_dict()
+            if isinstance(obj, TensorDataWrapper):
+                return obj.data_dict
+            if isinstance(obj, np.ndarray):
+                return {"data": obj.tolist(), "dtype": str(obj.dtype), "CLS": "numpy.array"}
+            if isinstance(obj, CalibrationMethod):
+                return {"CLS": obj.__class__.__name__, "value": str(obj)}
+            return json.JSONEncoder.default(self, obj)
+
+    json_data = json.dumps(calibration_cache, cls=MyEncoder)
+
+    with open(os.path.join(dir, "calibration.json"), "w") as file:
+        file.write(json_data)  # use `json.loads` to do the reverse
+
+    # Serialize data using FlatBuffers
+    zero = np.array(0)
+    builder = flatbuffers.Builder(1024)
+    key_value_list = []
+
+    for key in sorted(calibration_cache.keys()):
+        values = calibration_cache[key]
+        d_values = values.to_dict()
+
+        highest = d_values.get("highest", zero)
+        lowest = d_values.get("lowest", zero)
+
+        highest_val = highest.item() if hasattr(highest, "item") else float(highest)
+        lowest_val = lowest.item() if hasattr(lowest, "item") else float(lowest)
+
+        floats = [float(highest_val), float(lowest_val)]
+
+        value = str(max(floats))
+
+        flat_key = builder.CreateString(key)
+        flat_value = builder.CreateString(value)
+
+        KeyValue.KeyValueStart(builder)
+        KeyValue.KeyValueAddKey(builder, flat_key)
+        KeyValue.KeyValueAddValue(builder, flat_value)
+        key_value = KeyValue.KeyValueEnd(builder)
+
+        key_value_list.append(key_value)
+
+
+    TrtTable.TrtTableStartDictVector(builder, len(key_value_list))
+    for key_value in key_value_list:
+        builder.PrependUOffsetTRelative(key_value)
+    main_dict = builder.EndVector()
+
+    TrtTable.TrtTableStart(builder)
+    TrtTable.TrtTableAddDict(builder, main_dict)
+    cal_table = TrtTable.TrtTableEnd(builder)
+
+    builder.Finish(cal_table)
+    buf = builder.Output()
+
+    with open(os.path.join(dir, "calibration.flatbuffers"), "wb") as file:
+        file.write(buf)
+
+    # Deserialize data (for validation)
+    if os.environ.get("QUANTIZATION_DEBUG", 0) in (1, "1"):
+        cal_table = TrtTable.TrtTable.GetRootAsTrtTable(buf, 0)
+        dict_len = cal_table.DictLength()
+        for i in range(dict_len):
+            key_value = cal_table.Dict(i)
+            logging.info(key_value.Key())
+            logging.info(key_value.Value())
+
+    # write plain text
+    with open(os.path.join(dir, "calibration.cache"), "w") as file:
+        for key in sorted(calibration_cache.keys()):
+            values = calibration_cache[key]
+            d_values = values.to_dict()
+            highest = d_values.get("highest", zero)
+            lowest = d_values.get("lowest", zero)
+
+            highest_val = highest.item() if hasattr(highest, "item") else float(highest)
+            lowest_val = lowest.item() if hasattr(lowest, "item") else float(lowest)
+
+            floats = [float(highest_val), float(lowest_val)]
+
+            value = key + " " + str(max(floats))
+            file.write(value)
+            file.write("\n")
 
 def parse_input_args():
     parser = argparse.ArgumentParser()
@@ -553,8 +656,42 @@ def output_run_config(flags, samples):
         for k, v in compute_range.data.items():
             json_compute_range[k] = (float(v.range_value[0]), float(v.range_value[1]))
 
+        print("Writing calibration table")
+        try:
+            write_calibration_table(json_compute_range)
+        except AttributeError as e:
+            class TensorDataWrapper:
+                def __init__(self, data_dict):
+                    self.data_dict = data_dict
+
+                def to_dict(self):
+                    return self.data_dict
+
+                def __repr__(self):
+                    return repr(self.data_dict)
+
+                def __serializable__(self):
+                    return self.data_dict
+
+            calibration_data = {}
+            for k, v in compute_range.data.items():
+                if hasattr(v, 'to_dict'):
+                    tensor_dict = v.to_dict()
+                    processed_dict = {}
+                    for dk, dv in tensor_dict.items():
+                        if isinstance(dv, np.ndarray):
+                            processed_dict[dk] = dv.item() if dv.size == 1 else dv.tolist()
+                        elif isinstance(dv, np.number):
+                            processed_dict[dk] = dv.item()
+                        else:
+                            processed_dict[dk] = dv
+                    calibration_data[k] = TensorDataWrapper(processed_dict)
+                else:
+                    calibration_data[k] = v
+
+            print("Using custom calibration table function")
+            custom_write_calibration_table(calibration_data)
 
-        write_calibration_table(json_compute_range)
         print("Calibration is done. Calibration cache is saved to calibration.json")
 
         model_quants = model_quants + "_int8"