diff --git a/inference_models/inference_models/models/common/trt.py b/inference_models/inference_models/models/common/trt.py
index 2d62408930..7e78c907be 100644
--- a/inference_models/inference_models/models/common/trt.py
+++ b/inference_models/inference_models/models/common/trt.py
@@ -78,6 +78,7 @@ class TRTCudaGraphState:
     input_buffer: torch.Tensor
     output_buffers: List[torch.Tensor]
     execution_context: trt.IExecutionContext
+    consumer_done_event: Optional["torch.cuda.Event"] = None
 
 
 class TRTCudaGraphCache:
@@ -571,7 +572,10 @@ def infer_from_trt_engine(
             outputs=outputs,
             trt_cuda_graph_cache=trt_cuda_graph_cache,
         )
-    stream.synchronize()
+    graph_state = getattr(results[0], "_trt_graph_state", None)
+    produce_event = torch.cuda.Event()
+    produce_event.record(graph_state.cuda_stream if graph_state is not None else stream)
+    results[0]._trt_produce_event = produce_event
     return results
 
 
@@ -692,12 +696,16 @@ def _execute_trt_engine(
         if cache_key not in trt_cuda_graph_cache:
             LOGGER.debug("Capturing CUDA graph for shape %s", input_shape)
 
+            reuse_input = bool(
+                getattr(pre_processed_images, "_trt_reuse_as_input_buffer", False)
+            )
             results, trt_cuda_graph = _capture_cuda_graph(
                 pre_processed_images=pre_processed_images,
                 engine=engine,
                 device=device,
                 input_name=input_name,
                 outputs=outputs,
+                use_pre_processed_images_as_input_buffer=reuse_input,
             )
             trt_cuda_graph_cache[cache_key] = trt_cuda_graph
             return results
@@ -705,11 +713,18 @@ def _execute_trt_engine(
         else:
             trt_cuda_graph_state = trt_cuda_graph_cache[cache_key]
             stream = trt_cuda_graph_state.cuda_stream
+            consumer_done = trt_cuda_graph_state.consumer_done_event
+            if consumer_done is not None:
+                consumer_done.wait(stream)
             with torch.cuda.stream(stream):
-                trt_cuda_graph_state.input_buffer.copy_(pre_processed_images)
+                if (
+                    trt_cuda_graph_state.input_buffer.data_ptr()
+                    != pre_processed_images.data_ptr()
+                ):
+                    trt_cuda_graph_state.input_buffer.copy_(pre_processed_images)
                 trt_cuda_graph_state.cuda_graph.replay()
-                results = [buf.clone() for buf in trt_cuda_graph_state.output_buffers]
-            stream.synchronize()
+            results = list(trt_cuda_graph_state.output_buffers)
+            results[0]._trt_graph_state = trt_cuda_graph_state
             return results
 
     else:
@@ -752,14 +767,18 @@ def _capture_cuda_graph(
     device: torch.device,
     input_name: str,
     outputs: List[str],
+    use_pre_processed_images_as_input_buffer: bool = False,
 ) -> Tuple[List[torch.Tensor], TRTCudaGraphState]:
     # Each CUDA graph needs its own execution context. Sharing a single context
     # across graphs for different input shapes causes TRT to reallocate internal
     # workspace buffers, invalidating GPU addresses baked into earlier graphs.
     graph_context = engine.create_execution_context()
 
-    input_buffer = torch.empty_like(pre_processed_images, device=device)
-    input_buffer.copy_(pre_processed_images)
+    if use_pre_processed_images_as_input_buffer:
+        input_buffer = pre_processed_images
+    else:
+        input_buffer = torch.empty_like(pre_processed_images, device=device)
+        input_buffer.copy_(pre_processed_images)
 
     status = graph_context.set_input_shape(
         input_name, tuple(pre_processed_images.shape)