diff --git a/backends/cadence/aot/compiler.py b/backends/cadence/aot/compiler.py
index 5c66c9eb62b..ee1da44f391 100644
--- a/backends/cadence/aot/compiler.py
+++ b/backends/cadence/aot/compiler.py
@@ -152,6 +152,7 @@ def convert_pt2(
 def apply_pre_edge_transform_passes(
     converted_program: ExportedProgram,
     quantizer: CadenceQuantizer,
+    is_qat: bool = False,
 ) -> ExportedProgram:
     """
     Apply pre-edge transform passes including QuantFusion and torch ops passes.
@@ -166,12 +167,11 @@ def apply_pre_edge_transform_passes(
     """
     # pyre-ignore[16]: no attribute
     patterns = [q.pattern for q in quantizer.quantizers]
-    PassManager(
-        [
-            FuseQATConvBN(converted_program),
-            QuantFusion(patterns),
-        ]
-    )(converted_program.graph_module)
+    passes = []
+    if is_qat:
+        passes.append(FuseQATConvBN(converted_program))
+    passes.append(QuantFusion(patterns))
+    PassManager(passes)(converted_program.graph_module)
 
     # Apply torch ops passes (e.g., ReplaceMulTensorWithMulAndFullOpsPass)
     fused_program = apply_torch_ops_passes(converted_program)
@@ -187,19 +187,24 @@ def get_fake_quant_model(
     quantizer: CadenceQuantizer,
     calibration_data: Optional[list[tuple[object, ...]]] = None,
     dump_graphs: bool = False,
+    is_qat: bool = False,
 ) -> torch.fx.GraphModule:
     # Make the model inference mode by calling model.eval()
     model.eval()
 
     ops_to_keep = quantizer.get_ops_to_preserve_from_decomposition()
-    program = trace(model, inputs, dump_graphs=dump_graphs, ops_to_keep=ops_to_keep)
+    program = trace(
+        model, inputs, dump_graphs=dump_graphs, ops_to_keep=ops_to_keep, is_qat=is_qat
+    )
 
     if dump_graphs:
         logging.info("Graph after trace:")
         logging.info(program.graph.print_tabular())
 
     # Get prepared graph module
-    prepared_gm = prepare_traced_pt2(program, quantizer, dump_graphs=dump_graphs)
+    prepared_gm = prepare_traced_pt2(
+        program, quantizer, dump_graphs=dump_graphs, is_qat=is_qat
+    )
 
     # Calibrate
     # If no calibration data is provided, use the inputs
@@ -221,6 +226,7 @@ def quantize_pt2(
     calibration_data: Optional[list[tuple[object, ...]]] = None,
     dump_graphs: bool = False,
     quant_input_args: Optional[list[str]] = None,
+    is_qat: bool = False,
 ) -> ExportedProgram:
     """
     Trace, prepare, convert and fuse the model using the given quantizer.
@@ -242,6 +248,7 @@ def quantize_pt2(
         quantizer=quantizer,
         calibration_data=calibration_data,
         dump_graphs=dump_graphs,
+        is_qat=is_qat,
     )
     # Wrap the model to handle quantized inputs if provided
     if quant_input_args is not None:
@@ -254,7 +261,7 @@ def quantize_pt2(
     if quant_input_args is not None:
         QuantizedInputWrapper.sink_dequants(program)
 
-    fused_program = apply_pre_edge_transform_passes(program, quantizer)
+    fused_program = apply_pre_edge_transform_passes(program, quantizer, is_qat=is_qat)
 
     if dump_graphs:
         logging.info("Graph after quantization and fusion:")