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<h4> Easily achieve the best inference performance for any PyTorch model on the NVIDIA platform. </h4>
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[![Documentation](https://img.shields.io/badge/docs-master-brightgreen)](https://nvidia.github.io/Torch-TensorRT/)
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[![pytorch](https://img.shields.io/badge/PyTorch-2.4-green)](https://www.python.org/downloads/release/python-31013/)
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[![cuda](https://img.shields.io/badge/CUDA-12.4-green)](https://developer.nvidia.com/cuda-downloads)
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[![pytorch](https://img.shields.io/badge/PyTorch-2.8-green)](https://download.pytorch.org/whl/nightly/cu128)
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[![cuda](https://img.shields.io/badge/CUDA-12.8-green)](https://developer.nvidia.com/cuda-downloads)
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[![trt](https://img.shields.io/badge/TensorRT-10.9.0-green)](https://github.com/nvidia/tensorrt-llm)
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[![license](https://img.shields.io/badge/license-BSD--3--Clause-blue)](./LICENSE)
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[![linux_tests](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux.yml/badge.svg)](https://github.com/pytorch/TensorRT/actions/workflows/build-test-linux.yml)

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