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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
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Today
+1 stars today
This week
+15 stars this week
This month
+83 stars this month
Momentum
44.0
growth rate 0.11%/day
[](https://opensource.org/licenses/Apache-2.0) [](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html) [](documents/tensorrt_roadmap_2026q3.pdf) # :mega::mega: Announcement :mega::mega: TensorRT 11.X is now released with powerful new capabilities designed to accelerate your AI inference workflows. With this major version bump, TensorRT's API has been streamlined and a few legacy features from 10.X have been removed. Below provides migration guides for the following features: - Weakly-typed networks and related APIs have been removed, replaced by [Strongly Typed Networks](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/advanced.html#strongly-typed-networks). - Implicit quantization and related APIs have been removed, replaced by [Explicit Quantization](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/work-quantized-types.html#explicit-quantization) - IPluginV2 and related APIs have been removed, replaced by [IPluginV3](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/extending-custom-layers.html#migrating-v2-plugins-to-ipluginv3) - TREX tool has been removed, replaced by [Nsight Deep Learning Designer](https://docs.nvidia.com/nsight-dl-designer/UserGuide/index.html#visualizing-a-tensorrt-engine) - Python bindings for Python 3.9 and older versions have been removed. RPM packages for RHEL/Rocky Linux 8 and RHEL/Rocky Linux 9 now depend on Python 3.12. # TensorRT Open Source Software This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. It includes the sources for TensorRT plugins and ONNX parser, as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes. - For step-by-step walkthroughs of the TensorRT import paths (ONNX, Torch-TensorRT, HuggingFace/Optimum, Network Definition API) with examples and tooling tips, see the [Import Workflows Guide](documents/import_workflows.md). - For the per-model support matrix across import paths (LLM, encoder-NLP, vision, audio, diffusion, multimodal), see [Supported Models](documents/supported_models.md). - For code contributions to TensorRT-OSS, please see our [Contribution Guide](CONTRIBUTING.md) and [Coding Guidelines](CODING-GUIDELINES.md). - For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the [Changelog](CHANGELOG.md). - For business inquiries, please contact [[email protected]](mailto:[email protected]) - For press and other inquiries, please contact Hector Marinez at [[email protected]](mailto:[email protected]) Need enterprise support? NVIDIA global support is available for TensorRT with the [NVIDIA AI Enterprise software suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/). Check out [NVIDIA LaunchPad](https://www.nvidia.com/en-us/launchpad/ai/ai-enterprise/) for free access to a set of hands-on labs with TensorRT hosted on NVIDIA infrastructure. Join the [TensorRT and Triton community](https://www.nvidia.com/en-us/deep-learning-ai/triton-tensorrt-newsletter/) and stay current on the latest product updates, bug fixes, content, best practices, and more. # Agentic Coding Skills Various skills related to TensorRT usage and benchmarking are available [here](.agents/skills). For installation, refer to the instructions of your preferred coding agent. # Prebuilt TensorRT Python Package We provide the TensorRT Python package for an easy installation. \ To install: ```bash pip install tensorrt ``` You can skip the **Build** section to enjoy TensorRT with Python. # Build ## P
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Rajeev Rao · NVIDIA Corporation · United States
198
Kevin Chen · NVIDIA · United States
73
29
Xiaodong (Vincent) Huang · NVIDIA · United States
25
NVIDIA
23
Samurdhi Karunaratne · @nvidia · United States
22
Ilya Sherstyuk · Nvidia · United States
19
15
Yuan Yao · NVIDIA
13
13
Haohang Huang · NVIDIA
9
Gwena Cunha · NVIDIA · Canada
8
Ryan McCormick · @NVIDIA · United States
8
Luxiao Zheng · NVIDIA
8
Po-Wei (Vincent)
8
Simeng Liu
7
6
5
NVIDIA
5
5
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0951fe7c67803767, topic:inference, topic:tensorrt
matched fp:0951fe7c67803767, topic:deep-learning