# pytorch/TensorRT

PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT

Repository: https://github.com/pytorch/TensorRT
Canonical: https://ross.abutalabs.com/products/pytorch-tensorrt
Homepage: https://pytorch.org/TensorRT
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: tensorrt, libtorch, machine-learning, pytorch, deep-learning, jetson, nvidia, cuda
Last push: 2026-08-26T22:18:51+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 83, longevity 100
- inputs: {"age_days": 2366, "days_push": 7, "days_rel": 36, "gap_med": 57.5, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2986, forks 409 (observed 2026-08-28T04:07:34.135855+00:00)

## What it is
Torch-TensorRT is a compiler library that accelerates PyTorch model inference on NVIDIA GPUs using TensorRT. It supports just-in-time compilation via torch.compile and ahead-of-time export for deployment in Python or C++ (libtorch) environments.

## Use cases
- speed up pytorch model inference on nvidia gpus
- compile pytorch models with tensorrt backend
- deploy pytorch models in c++ without python dependency
- optimize inference latency for deep learning models
- export optimized torch models for production serving
- accelerate model inference on jetson devices

## When to choose
- you run PyTorch models on NVIDIA GPUs and need lower inference latency
- you want a one-line torch.compile backend for GPU acceleration
- you need to serialize optimized models for C++/libtorch deployment
- you target Jetson or other NVIDIA embedded platforms

## When to avoid
- you run inference on CPUs or non-NVIDIA GPUs
- you need training acceleration rather than inference
- your stack is TensorFlow, JAX, or ONNX Runtime based
- you cannot install CUDA and TensorRT dependencies

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-inference, compiler, gpu-computing
- domain: deep-learning, machine-learning, gpu-computing, developer-tools
- platform: windows, python, cpp
- tags: tensorrt, pytorch, nvidia, cuda, inference-optimization, torch-compile, model-deployment, jetson, linux, gpu

## Member repositories
- pytorch/TensorRT (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:34.135855+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:31:25.996870+00:00, confidence not recorded.
  - readme: https://github.com/pytorch/TensorRT (fetched 2026-08-28T04:07:34.135855+00:00, sha 7eb74e42a7f7)
  - homepage: https://pytorch.org/TensorRT (fetched 2026-08-29T09:46:31.015749+00:00, sha 2bcd1c5905f9)
  - site_page: https://docs.pytorch.org/TensorRT/getting_started/installation.html (fetched 2026-08-29T09:46:31.025303+00:00, sha dfbe6418e06d)
- Data as of 2026-08-30T08:39:29.467469+00:00.