diff --git a/userbenchmark/dynamo/dynamobench/common.py b/userbenchmark/dynamo/dynamobench/common.py
index c19578a3a7..96d356b8b3 100644
--- a/userbenchmark/dynamo/dynamobench/common.py
+++ b/userbenchmark/dynamo/dynamobench/common.py
@@ -707,6 +707,7 @@ def speedup_experiment_onnx(
     onnx_model_cls: Type[OnnxModelFromTorchScript],
     args,
     model_iter_fn,
+    onnx_model: OnnxModel,
     model,
     example_inputs,
     **kwargs,
@@ -715,9 +716,8 @@ def speedup_experiment_onnx(
     Measure speedups over eager.
 
     This function is responsible for the following:
-        1. Creation of OnnxModel, which handles export, ort initialization.
-        2. Creating iobinding with OnnxModel if device is CUDA, which is essential for perf measurement.
-        3. Running ORT with OnnxModel.
+        1. Creating iobinding with OnnxModel if device is CUDA, which is essential for perf measurement.
+        2. Running ORT with OnnxModel.
 
     Writes to ./{output_filename}, which should be
         `pathlib.Path(self.output_dir) / f"{self.compiler}_{suite}_{self.dtype}_{self.mode}_{self.device}_{self.testing}.csv".
@@ -729,16 +729,7 @@ def speedup_experiment_onnx(
     should_randomize_input = args.randomize_input
     times = args.iterations_per_run
 
-    onnx_model = onnx_model_cls(
-        args.output_directory or ".",
-        model,
-        copy.deepcopy(example_inputs),
-        dynamic_shapes=args.dynamic_shapes,
-    )
-
-    def create_onnx_input_binded_fn(
-        onnx_model: OnnxModelFromTorchScript, pt_inputs, example_outputs
-    ):
+    def create_onnx_input_binded_fn(onnx_model: OnnxModel, pt_inputs, example_outputs):
         # Goal is to move the iobinding creation outside of the timer function.
         iobinding, outputs = onnx_model.create_iobinding(pt_inputs, example_outputs)
 
@@ -749,7 +740,7 @@ def onnxrt_model_iter_fn(model, inputs, collect_outputs=True):
 
         return onnxrt_model_iter_fn
 
-    def create_onnx_fn(onnx_model: OnnxModelFromTorchScript, pt_inputs):
+    def create_onnx_fn(onnx_model: OnnxModel, pt_inputs):
         # NOTE: Making perf comparison fair by moving out the i/o adapting part.
         # 1. Pre-adapt `pt_inputs` to `onnx_inputs` here.
         # 2. Drop `onnx_outputs` to `pt_outputs` adapting. Output comparison is not part of perf measurement.
@@ -760,7 +751,7 @@ def onnxrt_model_iter_fn(model, inputs, collect_outputs=True):
 
         return onnxrt_model_iter_fn
 
-    def timed_onnx(model, onnx_model: OnnxModelFromTorchScript, inputs):
+    def timed_onnx(model, onnx_model: OnnxModel, inputs):
         if current_device == "cpu" or onnx_model.is_cpu():
             onnxrt_model_iter_fn = create_onnx_fn(onnx_model, inputs)
         else:
@@ -1526,9 +1517,14 @@ def parse_exception(self, exception: Exception) -> OnnxExportErrorRow:
         )
 
 
+@dataclasses.dataclass
+class OnnxContext:
+    onnx_model: Optional[OnnxModel] = None
+
+
 def optimize_onnx_ctx(
     output_directory: str,
-    onnx_model_cls: Type[OnnxModelFromTorchScript],
+    onnx_model_cls: Type[OnnxModel],
     run_n_iterations: Callable,
     dynamic_shapes: bool = False,
 ) -> Callable:
@@ -1537,7 +1533,8 @@ def optimize_onnx_ctx(
     #   1. Export and cache model.
     #   2. Create iobinding for ORT.
     #   3. Run ORT for n iterations.
-    onnx_model: Optional[OnnxModelFromTorchScript] = None
+    # The cached model is stored in 'context' under the returned callable.
+    context = OnnxContext()
     test_data_dumped = False
 
     def run_n_iterations_onnx(model, inputs, n=2):
@@ -1553,14 +1550,15 @@ def run_n_iterations_onnx(model, inputs, n=2):
         output_error_filename = output_filename[:-4] + "_export_error.csv"
         parser = OnnxExportErrorParser(current_device, current_name, current_batch_size)
         try:
-            nonlocal onnx_model
-            if onnx_model is None:
-                onnx_model = onnx_model_cls(
+            nonlocal context
+            if context.onnx_model is None:
+                context.onnx_model = onnx_model_cls(
                     output_directory,
                     model,
                     copy.deepcopy(inputs),
                     dynamic_shapes=dynamic_shapes,
                 )
+            onnx_model = context.onnx_model
 
             for _ in range(n):
                 nonlocal test_data_dumped
@@ -1598,6 +1596,8 @@ def run_n_iterations_onnx(model, inputs, n=2):
             output_csv(output_error_filename, parsed_error.headers, parsed_error.row)
             raise
 
+    run_n_iterations_onnx.context = context
+
     return run_n_iterations_onnx
 
 
@@ -2473,6 +2473,11 @@ def warmup(fn, model, example_inputs, mode, niters=5):
                     f"{ok:3}/{total:3} +{frames_third_pass} frames {compilation_time:3.0f}s"
                 )
 
+            if experiment.func is speedup_experiment_onnx:
+                experiment = functools.partial(
+                    experiment, optimized_model_iter_fn.context.onnx_model
+                )
+
             if not hasattr(model, name):
                 model.name = name
             results.append(experiment(model, example_inputs, **experiment_kwargs))
@@ -3356,9 +3361,7 @@ def run(runner, args, original_dir=None):
         optimize_ctx = functools.partial(
             optimize_onnx_ctx, args.output_directory or ".", OnnxModelFromTorchScript
         )
-        experiment = functools.partial(
-            speedup_experiment_onnx, OnnxModelFromTorchScript
-        )
+        experiment = speedup_experiment_onnx
         output_filename = "torchscript_onnx.csv"
         current_onnx_compiler = "torchscript"
     elif args.dynamo_onnx:
@@ -3368,7 +3371,7 @@ def run(runner, args, original_dir=None):
             OnnxModelFromDynamo,
             dynamic_shapes=args.dynamic_shapes,
         )
-        experiment = functools.partial(speedup_experiment_onnx, OnnxModelFromDynamo)
+        experiment = speedup_experiment_onnx
         output_filename = "dynamo_onnx.csv"
         current_onnx_compiler = "dynamo"
     elif args.dynamo_onnx_aot_inline:
@@ -3378,9 +3381,7 @@ def run(runner, args, original_dir=None):
             OnnxModelFromDynamoAotInline,
             dynamic_shapes=args.dynamic_shapes,
         )
-        experiment = functools.partial(
-            speedup_experiment_onnx, OnnxModelFromDynamoAotInline
-        )
+        experiment = speedup_experiment_onnx
         output_filename = "dynamo_onnx_aot_inline.csv"
         current_onnx_compiler = "dynamo"
     elif args.speedup_dynamo_ts: