TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.

A Datacenter Scale Distributed Inference Serving Framework

Reports junit test results as GitHub Pull Request Check

✨ A Github Action which sets the base and head SHAs required for `nx affected` commands in CI

Github action to retrieve all (added, copied, modified, deleted, renamed, type changed, unmerged, unknown) files and directories.

A GitHub action to create a pull request for changes to your repository in the actions workspace

A unified library of state-of-the-art model optimization techniques such as quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed on NVIDIA GPUs.