Practical overview
When it fits
Use TensorRT when preparing deep-learning models for inference on NVIDIA GPUs or integrating optimized inference into an application. Consult the import walkthroughs and model support matrix when selecting a model path.
Who it is for
- Developers integrating GPU-accelerated deep-learning inference into applications.
- C++ and Python developers evaluating TensorRT model import and inference workflows.
- Teams checking documented model support across language, vision, audio, diffusion and multimodal workloads.
Documented uses
- Optimize deep-learning models for inference on NVIDIA GPUs.
- Integrate inference into server or other applications.
- Use the documented ONNX, Torch-TensorRT, Hugging Face/Optimum or Network Definition API import paths.
- Review sample applications and OSS components when developing with TensorRT.
Requirements & limits
The TensorRT repository is public and identifies its open-source components as Apache-2.0 licensed. It also describes a prebuilt Python package; the repository does not contain the full TensorRT GA release.
- The documented target is inference on NVIDIA GPUs.
- The repository's OSS code is a subset of the TensorRT GA release, rather than the complete distribution.
- A prebuilt Python package is available; building OSS components has a separate prerequisite list.
- The README describes API and tool removals in TensorRT 11.x and points to migration guidance.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the TensorRT-OSS build environment
Set up the repository and its documented prerequisites before proceeding with a TensorRT-OSS build.
Before you start
- TensorRT GA build; the README lists TensorRT v11.4.0.106.
- CUDA; recommended versions listed are 13.4.0 and 12.9.0.
- GNU make 4.1 or later, CMake 3.31 or later, Python 3.10 through 3.14.x, and pip 19.0 or later.
- Git, pkg-config and wget.
Steps
Install the listed required system packages and obtain the TensorRT GA build.
Clone the TensorRT repository on its main branch.
Initialize and update the repository's submodules.
Expected outcome
The TensorRT-OSS source tree and its submodules are present, with the listed build prerequisites identified.
Guide limitations
- These steps cover source and prerequisite setup, not compiling or running TensorRT.
- cuDNN is optional; NCCL is only required for the documented multi-device sample. The README also describes a containerized build option.
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 |
|---|---|---|---|
| Tensorrt | Optimize deep-learning models for inference on NVIDIA GPUs. | Linux, Self-hosted, Server, Windows | The TensorRT repository is public and identifies its open-source components as Apache-2.0 licensed. It also describes a prebuilt Python package; the repository does not contain the full TensorRT GA release. |
| 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
- 13 October 2026
- Original website title
- NVIDIA/TensorRT · Official homepage title · retained in its source language