Practical overview
When it fits
Use it when an existing PyTorch inference workflow needs a TensorRT compilation path on NVIDIA GPUs, either at runtime or through an exported artifact.
Who it is for
- PyTorch developers seeking TensorRT-optimized GPU inference.
- Teams preparing optimized model artifacts for PyTorch or libtorch-based C++ deployment.
Documented uses
- Compile a PyTorch model at runtime through the torch.compile TensorRT backend.
- Optimize a model ahead of time using representative inputs and serialize it.
- Load an exported optimized module in PyTorch or deploy a TorchScript file with libtorch.
Requirements & limits
The project documents stable installation through PyPI, nightly packages through the PyTorch package index, and distribution in an NVIDIA NGC PyTorch container. It is licensed under BSD-3-Clause.
- The documented inference path targets NVIDIA GPUs and uses CUDA, TensorRT, and PyTorch.
- Runtime compilation uses torch.compile and compiles on the first model invocation in the example.
- Ahead-of-time export requires representative inputs; TorchScript is the documented format for C++ deployment.
- The README also identifies an NGC PyTorch container with dependencies and example notebooks.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Compile a PyTorch model at runtime with torch.compile
Use the TensorRT backend with an evaluation-mode PyTorch model and invoke it on a CUDA input.
Before you start
- A PyTorch model and input tensor
- CUDA-capable NVIDIA GPU
- Torch-TensorRT installed in the PyTorch environment
Steps
Put the model in evaluation mode and move it to CUDA.
Create the input tensor on CUDA.
Call torch.compile(model, backend="tensorrt") to create the optimized model.
Invoke the optimized model with the input; the documented example compiles on the first run.
Expected outcome
A model routed through the TensorRT backend and callable from the PyTorch application.
Guide limitations
- The example uses a CUDA model and input on an NVIDIA GPU.
- The documented example does not specify model-specific compatibility or performance results.
Export an optimized module for PyTorch or C++ deployment
Compile a model with representative inputs, serialize it, then load the appropriate exported format in PyTorch or libtorch.
Before you start
- A PyTorch model in evaluation mode
- Representative CUDA input tensors
- Torch-TensorRT and the relevant PyTorch or libtorch runtime
Steps
Compile the model with torch_tensorrt.compile using ir="dynamo" and representative inputs.
Save an ExportedProgram file for PyTorch use, or save with output_format="torchscript" for C++ deployment.
For PyTorch, load the ExportedProgram and call the loaded module with inputs.
For C++, load the TorchScript file with torch::jit::load and call forward with input tensors.
Expected outcome
A serialized optimized module that can be loaded in PyTorch, or a TorchScript module intended for libtorch-based C++ deployment.
Guide limitations
- The README notes that PyTorch supports Python runtime for an ExportedProgram; use TorchScript for C++ deployment.
- The provided examples assume CUDA inputs and do not establish compatibility for every model or target environment.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Torch-TensorRT | Compile a PyTorch model at runtime through the torch.compile TensorRT backend. | Linux, macOS, Server, Windows | The project documents stable installation through PyPI, nightly packages through the PyTorch package index, and distribution in an NVIDIA NGC PyTorch container. It is licensed under BSD-3-Clause. |
| Cloud AI SDK | Adapt and run documented model recipes for computer-vision and NLP inference. | Cloud, Server | The GitHub repository is public. Its public visibility does not establish license terms. |
| Turboquant | Replace a model’s existing inference cache with a compressed cache. | Cloud, Linux, macOS, Self-hosted | The project describes itself as open source. The README documents installation through pip or from a source checkout. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 10 October 2026
- Original website title
- pytorch/TensorRT · Official homepage title · retained in its source language