TensorRT-LLM

A TensorRT Toolbox for Optimized Large Language Model Inference

Architecture | Results | Examples | Documentation

Latest News

[2024/10/22] New ? Step-by-step instructions on how to ✅ Optimize LLMs with NVIDIA TensorRT-LLM, ✅ Deploy the optimized models with Triton Inference Server, ✅ Autoscale LLMs deployment in a Kubernetes environment. ? Technical Deep Dive: ➡️ link

[2024/10/07] Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries ➡️ link
[2024/09/29] AI at Meta PyTorch + TensorRT v2.4 ? ⚡TensorRT 10.1 ⚡PyTorch 2.4 ⚡CUDA 12.4 ⚡Python 3.12 ➡️ link
[2024/09/17] NVIDIA TensorRT-LLM Meetup ➡️ link
[2024/09/17] Accelerating LLM Inference at Databricks with TensorRT-LLM ➡️ link
[2024/09/17] TensorRT-LLM @ Baseten ➡️ link
[2024/09/04] Best Practices for Tuning TensorRT-LLM for Optimal Serving with BentoML ➡️ link
[2024/08/20] SDXL with #TensorRT Model Optimizer ⏱️⚡ ? cache diffusion ? quantization aware training ? QLoRA ? #Python 3.12 ➡️ link
[2024/08/13] DIY Code Completion with #Mamba ⚡ #TensorRT #LLM for speed ? NIM for ease ☁️ deploy anywhere ➡️ link
[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

Previous News

[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
[2024/05/28] #TensorRT weight stripping for ResNet-50 ✨ ✅+99% compression ✅1 set of weights → ** GPUs ✅0 performance loss ✅** models…LLM, CNN, etc ? ? DIY ➡️ link
[2024/05/21] @modal_labs has the codes for serverless @AIatMeta Llama 3 on #TensorRT #LLM ✨? ? Marvelous Modal Manual: Serverless TensorRT-LLM (LLaMA 3 8B) | Modal Docs ➡️ link
[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
[2024/05/07] 24,000 tokens per second ?Meta Llama 3 takes off with #TensorRT #LLM ?➡️ link
[2024/02/06] Speed up inference with SOTA quantization techniques in TRT-LLM
[2024/01/30] New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget
[2023/12/04] Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100
[2023/11/27] SageMaker LMI now supports TensorRT-LLM - improves throughput by 60%, compared to previous version
[2023/11/13] H200 achieves nearly 12,000 tok/sec on Llama2-13B
[2023/10/22] RAG on Windows using TensorRT-LLM and LlamaIndex ?
[2023/10/19] Getting Started Guide - Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available
[2023/10/17] Large Language Models up to 4x Faster on RTX With TensorRT-LLM for Windows

TensorRT-LLM Overview

TensorRT-LLM is a library for optimizing Large Language Model (LLM) inference. It provides state-of-the-art optimizations, including custom attention kernels, inflight batching, paged KV caching, quantization (FP8, INT4 AWQ, INT8 SmoothQuant, ++) and much more, to perform inference efficiently on NVIDIA GPUs

TensorRT-LLM provides a Python API to build LLMs into optimized TensorRT engines. It contains runtimes in Python (bindings) and C++ to execute those TensorRT engines. It also includes a backend for integration with the NVIDIA Triton Inference Server. 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 and/or Pipeline Parallelism).

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

TensorRT-LLM is built on top of the 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" 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.