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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.

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TensorRT-LLM

A TensorRT Toolbox for Optimized Large Language Model Inference

Documentation python cuda trt version license

Architecture   |   Results   |   Examples   |   Documentation


Latest News

  • [2024/08/06] 🗫 Multilingual Challenge Accepted 🗫 🤖 #TensorRT #LLM boosts low-resource languages like Hebrew, Indonesian and Vietnamese ⚡➡️ link
  • [2024/07/30] Introducing🍊 @SliceXAI ELM Turbo 🤖 train ELM once ⚡ #TensorRT #LLM optimize ☁️ deploy anywhere ➡️ link

  • [2024/07/23] 👀 @AIatMeta Llama 3.1 405B trained on 16K NVIDIA H100s - inference is #TensorRT #LLM optimized ⚡ 🦙 400 tok/s - per node 🦙 37 tok/s - per user 🦙 1 node inference ➡️ link

  • [2024/07/09] Checklist to maximize multi-language performance of @meta #Llama3 with #TensorRT #LLM inference: ✅ MultiLingual ✅ NIM ✅ LoRA tuned adaptors➡️ Tech blog

  • [2024/07/02] Let the @MistralAI MoE tokens fly 📈 🚀 #Mixtral 8x7B with NVIDIA #TensorRT #LLM on #H100. ➡️ Tech blog

  • [2024/06/24] Enhanced with NVIDIA #TensorRT #LLM, @upstage.ai’s solar-10.7B-instruct is ready to power your developer projects through our API catalog 🏎️. ✨➡️ link

  • [2024/06/18] CYMI: 🤩 Stable Diffusion 3 dropped last week 🎊 🏎️ Speed up your SD3 with #TensorRT INT8 Quantization➡️ link

  • [2024/06/18] 🧰Deploying ComfyUI with TensorRT? Here’s your setup guide ➡️ link

  • [2024/06/11] ✨#TensorRT Weight-Stripped Engines ✨ Technical Deep Dive for serious coders ✅+99% compression ✅1 set of weights → ** GPUs ✅0 performance loss ✅** models…LLM, CNN, etc.➡️ link

  • [2024/06/04] ✨ #TensorRT and GeForce #RTX unlock ComfyUI SD superhero powers 🦸⚡ 🎥 Demo: ➡️ link 📗 DIY notebook: ➡️ link

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TensorRT-LLM Overview

TensorRT-LLM is 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 contains components to create Python and C++ runtimes that execute those TensorRT engines. It also includes a backend for integration with the NVIDIA Triton Inference Server; a production-quality system to serve LLMs. Models built with TensorRT-LLM can be executed on a wide range of configurations going from a single GPU to multiple nodes with multiple GPUs (using Tensor Parallelism and/or Pipeline Parallelism).

The TensorRT-LLM Python API architecture looks similar to the PyTorch API. It provides a functional module containing functions like einsum, softmax, matmul or view. The layers module bundles useful building blocks to assemble LLMs; like an Attention block, a MLP or the entire Transformer layer. Model-specific components, like GPTAttention or BertAttention, can be found in the models module.

TensorRT-LLM comes with several popular models pre-defined. They can easily be modified and extended to fit custom needs. Refer to the Support Matrix for a list of supported models.

To maximize performance and reduce memory footprint, TensorRT-LLM allows the models to be executed using different quantization modes (refer to support matrix). TensorRT-LLM supports INT4 or INT8 weights (and FP16 activations; a.k.a. INT4/INT8 weight-only) as well as a complete implementation of the SmoothQuant technique.

Getting Started

To get started with TensorRT-LLM, visit our documentation:

Community

  • Model zoo (generated by TRT-LLM rel 0.9 a9356d4b7610330e89c1010f342a9ac644215c52)

About

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.

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