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https://github.com/NVIDIA/TensorRT-LLM.git
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* Update TensorRT-LLM --------- Co-authored-by: Sherlock Xu <65327072+Sherlock113@users.noreply.github.com>
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120 lines
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TensorRT-LLM
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===========================
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<h4> A TensorRT Toolbox for Optimized Large Language Model Inference</h4>
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[](https://nvidia.github.io/TensorRT-LLM/)
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[](https://www.python.org/downloads/release/python-31012/)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://developer.nvidia.com/tensorrt)
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[](./tensorrt_llm/version.py)
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[](./LICENSE)
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[Architecture](./docs/source/architecture/overview.md) | [Results](./docs/source/performance/perf-overview.md) | [Examples](./examples/) | [Documentation](./docs/source/)
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---
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<div align="left">
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## Latest News
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* [2024/09/04] 🏎️🏎️🏎️ Best Practices for Tuning TensorRT-LLM for Optimal Serving with BentoML
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[➡️ link](https://www.bentoml.com/blog/tuning-tensor-rt-llm-for-optimal-serving-with-bentoml)
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* [2024/08/20] 🏎️SDXL with #TensorRT Model Optimizer ⏱️⚡ 🏁 cache diffusion 🏁 quantization aware training 🏁 QLoRA 🏁 #Python 3.12
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[➡️ link](https://developer.nvidia.com/blog/nvidia-tensorrt-model-optimizer-v0-15-boosts-inference-performance-and-expands-model-support/)
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* [2024/08/13] 🐍 DIY Code Completion with #Mamba ⚡ #TensorRT #LLM for speed 🤖 NIM for ease ☁️ deploy anywhere
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[➡️ link](https://developer.nvidia.com/blog/revolutionizing-code-completion-with-codestral-mamba-the-next-gen-coding-llm/)
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* [2024/08/06] 🗫 Multilingual Challenge Accepted 🗫
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🤖 #TensorRT #LLM boosts low-resource languages like Hebrew, Indonesian and Vietnamese ⚡[➡️ link](https://developer.nvidia.com/blog/accelerating-hebrew-llm-performance-with-nvidia-tensorrt-llm/?linkId=100000278659647)
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* [2024/07/30] Introducing🍊 @SliceXAI ELM Turbo 🤖 train ELM once ⚡ #TensorRT #LLM optimize ☁️ deploy anywhere
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[➡️ link](https://developer.nvidia.com/blog/supercharging-llama-3-1-across-nvidia-platforms)
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* [2024/07/23] 👀 @AIatMeta Llama 3.1 405B trained on 16K NVIDIA H100s - inference is #TensorRT #LLM optimized ⚡
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🦙 400 tok/s - per node
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🦙 37 tok/s - per user
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🦙 1 node inference
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[➡️ link](https://developer.nvidia.com/blog/supercharging-llama-3-1-across-nvidia-platforms)
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* [2024/07/09] Checklist to maximize multi-language performance of @meta #Llama3 with #TensorRT #LLM inference:
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✅ MultiLingual
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✅ NIM
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✅ LoRA tuned adaptors[➡️ Tech blog](https://developer.nvidia.com/blog/deploy-multilingual-llms-with-nvidia-nim/)
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* [2024/07/02] Let the @MistralAI MoE tokens fly 📈 🚀 #Mixtral 8x7B with NVIDIA #TensorRT #LLM on #H100.
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[➡️ Tech blog](https://developer.nvidia.com/blog/achieving-high-mixtral-8x7b-performance-with-nvidia-h100-tensor-core-gpus-and-tensorrt-llm?ncid=so-twit-928467)
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* [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](https://build.nvidia.com/upstage/solar-10_7b-instruct?snippet_tab=Try )
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* [2024/06/18] CYMI: 🤩 Stable Diffusion 3 dropped last week 🎊 🏎️ Speed up your SD3 with #TensorRT INT8 Quantization[➡️ link](https://build.nvidia.com/upstage/solar-10_7b-instruct?snippet_tab=Try )
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* [2024/06/18] 🧰Deploying ComfyUI with TensorRT? Here’s your setup guide [➡️ link](https://github.com/comfyanonymous/ComfyUI_TensorRT)
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* [2024/06/11] ✨#TensorRT Weight-Stripped Engines ✨
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Technical Deep Dive for serious coders ✅+99% compression ✅1 set of weights → ** GPUs ✅0 performance loss ✅** models…LLM, CNN, etc.[➡️ link](https://developer.nvidia.com/blog/maximum-performance-and-minimum-footprint-for-ai-apps-with-nvidia-tensorrt-weight-stripped-engines/)
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* [2024/06/04] ✨ #TensorRT and GeForce #RTX unlock ComfyUI SD superhero powers 🦸⚡ 🎥 Demo: [➡️ link](https://youtu.be/64QEVfbPHyg)
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📗 DIY notebook: [➡️ link](https://console.brev.dev/launchable/deploy?userID=2x2sil999&orgID=ktj33l4xj&name=ComfyUI_TensorRT&instance=L4%40g2-standard-4%3Anvidia-l4%3A1&diskStorage=500&cloudID=GCP&baseImage=docker.io%2Fpytorch%2Fpytorch%3A2.2.0-cuda12.1-cudnn8-runtime&ports=ComfUI%3A8188&file=https%3A%2F%2Fgithub.com%2Fbrevdev%2Fnotebooks%2Fblob%2Fmain%2Ftensorrt-comfyui.ipynb&launchableID=env-2hQX3n7ae5mq3NjNZ32DfAG0tJf)
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<details close>
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<summary>Previous News</summary>
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* [2024/05/28] ✨#TensorRT weight stripping for ResNet-50 ✨ ✅+99% compression
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✅1 set of weights → ** GPUs\ ✅0 performance loss ✅** models…LLM, CNN, etc
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👀 📚 DIY [➡️ link](https://console.brev.dev/launchable/deploy?userID=2x2sil999&orgID=ktj33l4xj&launchableID=env-2h6bym7h5GFNho3vpWQQeUYMwTM&instance=L4%40g6.xlarge&diskStorage=500&cloudID=devplane-brev-1&baseImage=nvcr.io%2Fnvidia%2Ftensorrt%3A24.05-py3&file=https%3A%2F%2Fgithub.com%2FNVIDIA%2FTensorRT%2Fblob%2Frelease%2F10.0%2Fsamples%2Fpython%2Fsample_weight_stripping%2Fnotebooks%2Fweight_stripping.ipynb&name=tensorrt_weight_stripping_resnet50)
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* [2024/05/21] ✨@modal_labs has the codes for serverless @AIatMeta Llama 3 on #TensorRT #LLM ✨👀 📚 Marvelous Modal Manual:
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Serverless TensorRT-LLM (LLaMA 3 8B) | Modal Docs [➡️ link](https://modal.com/docs/examples/trtllm_llama)
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* [2024/05/08] NVIDIA TensorRT Model Optimizer -- the newest member of the #TensorRT ecosystem is a library of post-training and training-in-the-loop model optimization techniques ✅quantization ✅sparsity ✅QAT [➡️ blog](https://developer.nvidia.com/blog/accelerate-generative-ai-inference-performance-with-nvidia-tensorrt-model-optimizer-now-publicly-available/)
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* [2024/05/07] 🦙🦙🦙 24,000 tokens per second 🛫Meta Llama 3 takes off with #TensorRT #LLM 📚[➡️ link](https://blogs.nvidia.com/blog/meta-llama3-inference-acceleration/)
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* [2024/02/06] [🚀 Speed up inference with SOTA quantization techniques in TRT-LLM](./docs/source/blogs/quantization-in-TRT-LLM.md)
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* [2024/01/30] [ New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget](./docs/source/blogs/XQA-kernel.md)
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* [2023/12/04] [Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100](./docs/source/blogs/Falcon180B-H200.md)
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* [2023/11/27] [SageMaker LMI now supports TensorRT-LLM - improves throughput by 60%, compared to previous version](https://aws.amazon.com/blogs/machine-learning/boost-inference-performance-for-llms-with-new-amazon-sagemaker-containers/)
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* [2023/11/13] [H200 achieves nearly 12,000 tok/sec on Llama2-13B](./docs/source/blogs/H200launch.md)
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* [2023/10/22] [🚀 RAG on Windows using TensorRT-LLM and LlamaIndex 🦙](https://github.com/NVIDIA/trt-llm-rag-windows#readme)
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* [2023/10/19] Getting Started Guide - [Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available
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](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/)
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* [2023/10/17] [Large Language Models up to 4x Faster on RTX With TensorRT-LLM for Windows
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](https://blogs.nvidia.com/blog/2023/10/17/tensorrt-llm-windows-stable-diffusion-rtx/)
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</details>
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## TensorRT-LLM Overview
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TensorRT-LLM is a library for optimizing Large Language Model (LLM) inference.
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It provides state-of-the-art optimziations, including custom attention kernels, inflight batching, paged KV caching, quantization (FP8, INT4 [AWQ](https://arxiv.org/abs/2306.00978), INT8 [SmoothQuant](https://arxiv.org/abs/2211.10438), ++) and much more, to perform inference efficiently on NVIDIA GPUs
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TensorRT-LLM provides a Python API to build LLMs into optimized
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[TensorRT](https://developer.nvidia.com/tensorrt) engines.
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It contains runtimes in Python (bindings) and C++ to execute those TensorRT engines.
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It also includes a [backend](https://github.com/triton-inference-server/tensorrtllm_backend) for integration with the [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server).
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Models built with TensorRT-LLM can be executed on a wide range of configurations from a single GPU to multiple nodes with multiple GPUs (using [Tensor Parallelism](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/features/parallelisms.html#tensor-parallelism) and/or [Pipeline Parallelism](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/features/parallelisms.html#pipeline-parallelism)).
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TensorRT-LLM comes with several popular models pre-defined. They can easily be
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modified and extended to fit custom needs via a PyTorch-like Python API. Refer to the [Support Matrix](https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html) for a list of supported models.
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TensorRT-LLM is built on top of the [TensorRT](https://developer.nvidia.com/tensorrt) Deep Learning Inference library. It leverages much of TensorRT's deep learning optimizations and adds LLM-specific optimizations on top, as described above. TensorRT is an ahead-of-time compiler; it builds "[Engines](https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/index.html#ecosystem)" which are optimized representations of the compiled model containing the entire execution graph. These engines are optimized for a specific GPU architecture, and can be validated, benchmarked, and serialized for later deployment in a production environment.
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## Getting Started
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To get started with TensorRT-LLM, visit our documentation:
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- [Quick Start Guide](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html)
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- [Release Notes](https://nvidia.github.io/TensorRT-LLM/release-notes.html)
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- [Installation Guide for Linux](https://nvidia.github.io/TensorRT-LLM/installation/linux.html)
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- [Installation Guide for Windows](https://nvidia.github.io/TensorRT-LLM/installation/windows.html)
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- [Supported Hardware, Models, and other Software](https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html)
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## Community
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- [Model zoo](https://huggingface.co/TheFloat16) (generated by TRT-LLM rel 0.9 a9356d4b7610330e89c1010f342a9ac644215c52)
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