diff --git a/examples/apple/coreml/scripts/export.py b/examples/apple/coreml/scripts/export.py
index d1d4a4bcca2..1aa5806e371 100644
--- a/examples/apple/coreml/scripts/export.py
+++ b/examples/apple/coreml/scripts/export.py
@@ -13,20 +13,14 @@
 import executorch.exir as exir
 
 import torch
-from coremltools.optimize.torch.quantization.quantization_config import (
-    LinearQuantizerConfig,
-    QuantizationScheme,
-)
 
 from executorch.backends.apple.coreml.compiler import CoreMLBackend
 
 from executorch.backends.apple.coreml.partition import CoreMLPartitioner
-from executorch.backends.apple.coreml.quantizer import CoreMLQuantizer
 from executorch.devtools.etrecord import generate_etrecord
 from executorch.exir import to_edge
 
 from executorch.exir.backend.backend_api import to_backend
-from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
 
 from torch.export import export
 
@@ -81,13 +75,6 @@ def parse_args() -> argparse.ArgumentParser:
     parser.add_argument("--generate_etrecord", action=argparse.BooleanOptionalAction)
     parser.add_argument("--save_processed_bytes", action=argparse.BooleanOptionalAction)
 
-    parser.add_argument(
-        "--quantize",
-        action=argparse.BooleanOptionalAction,
-        required=False,
-        help="Quantize CoreML model",
-    )
-
     args = parser.parse_args()
     return args
 
@@ -123,10 +110,9 @@ def export_lowered_module_to_executorch_program(lowered_module, example_inputs):
     return exec_prog
 
 
-def save_executorch_program(exec_prog, model_name, compute_unit, quantize):
+def save_executorch_program(exec_prog, model_name, compute_unit):
     buffer = exec_prog.buffer
-    data_type = "quantize" if quantize else "fp"
-    filename = f"{model_name}_coreml_{compute_unit}_{data_type}.pte"
+    filename = f"{model_name}_coreml_{compute_unit}.pte"
     print(f"Saving exported program to {filename}")
     with open(filename, "wb") as file:
         file.write(buffer)
@@ -182,22 +168,6 @@ def main():
     if args.use_partitioner:
         model.eval()
         exir_program_aten = torch.export.export(model, example_inputs)
-        if args.quantize:
-            quantization_config = LinearQuantizerConfig.from_dict(
-                {
-                    "global_config": {
-                        "quantization_scheme": QuantizationScheme.affine,
-                        "activation_dtype": torch.quint8,
-                        "weight_dtype": torch.qint8,
-                        "weight_per_channel": True,
-                    }
-                }
-            )
-
-            quantizer = CoreMLQuantizer(quantization_config)
-            model = prepare_pt2e(model, quantizer)  # pyre-fixme[6]
-            model(*example_inputs)
-            exir_program_aten = convert_pt2e(model)
 
         edge_program_manager = exir.to_edge(exir_program_aten)
         edge_copy = copy.deepcopy(edge_program_manager)
@@ -219,9 +189,7 @@ def main():
             example_inputs,
         )
 
-    save_executorch_program(
-        exec_program, args.model_name, args.compute_unit, args.quantize
-    )
+    save_executorch_program(exec_program, args.model_name, args.compute_unit)
     generate_etrecord(f"{args.model_name}_coreml_etrecord.bin", edge_copy, exec_program)
 
     if args.save_processed_bytes and lowered_module is not None: