NVIDIA/TensorRT-LLM

Tech Blogs

• [10/14] KV Cache Compression in TensorRT LLM

• [09/22] GVR V2: Self-Sampling and Multi-Thresholding for Faster Exact Top-K

• [09/02] Accelerating Video Generation with GEMM Quantization, Attention Quantization and Skip Softmax Attention in TensorRT-LLM

• [08/20] Evaluating Agentic Serving with Trace Replay and Job-Level Metrics

• [07/17] DeepSeek-V4 on NVIDIA Blackwell: Model-Specific and Agentic-Workload Optimizations in TensorRT LLM

• [07/01] Scaling Video Generation Across NVL72 Rack with TensorRT-LLM

• [05/15] Joint Optimization of Agent Applications and TensorRT-LLM

• [04/03] Tuning CUDA Graph Batch Sizes for Higher Output Throughput

• [04/03] DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72

• [03/16] Optimizing MoE Communication with One-Sided AlltoAll Over NVLink

• [03/04] Sparse Attention in TensorRT LLM

• [02/06] Accelerating Long-Context Inference with Skip Softmax Attention

• [01/09] Optimizing DeepSeek-V3.2 on NVIDIA Blackwell GPUs

Latest News

• [04/03] 🎨 TensorRT LLM now supports diffusion models for visual generation ➡️ link

Previous News

• [08/05] 🌟 TensorRT LLM delivers Day-0 support for OpenAI's latest open-weights models: GPT-OSS-120B ➡️ link and GPT-OSS-20B ➡️ link
• [07/15] 🌟 TensorRT LLM delivers Day-0 support for LG AI Research's latest model, EXAONE 4.0 ➡️ link
• [05/22] Blackwell Breaks the 1,000 TPS/User Barrier With Meta’s Llama 4 Maverick

✨ ➡️ link

• [04/10] TensorRT LLM DeepSeek R1 performance benchmarking best practices now published.

✨ ➡️ link

• [04/05] TensorRT LLM can run Llama 4 at over 40,000 tokens per second on B200 GPUs!

• [03/22] TensorRT LLM is now fully open-source, with developments moved to GitHub!
• [03/18] 🚀🚀 NVIDIA Blackwell Delivers World-Record DeepSeek-R1 Inference Performance with TensorRT LLM ➡️ Link
• [02/28] 🌟 NAVER Place Optimizes SLM-Based Vertical Services with TensorRT LLM ➡️ Link
• [02/25] 🌟 DeepSeek-R1 performance now optimized for Blackwell ➡️ Link
• [02/20] Explore the complete guide to achieve great accuracy, high throughput, and low latency at the lowest cost for your business here.
• [02/18] Unlock #LLM inference with auto-scaling on @AWS EKS ✨ ➡️ link
• [02/12] 🦸⚡ Automating GPU Kernel Generation with DeepSeek-R1 and Inference Time Scaling

➡️ link

• [02/12] 🌟 How Scaling Laws Drive Smarter, More Powerful AI

➡️ link

• [2025/01/25] Nvidia moves AI focus to inference cost, efficiency ➡️ link
• [2025/01/24] 🏎️ Optimize AI Inference Performance with NVIDIA Full-Stack Solutions ➡️ link
• [2025/01/23] 🚀 Fast, Low-Cost Inference Offers Key to Profitable AI ➡️ link
• [2025/01/16] Introducing New KV Cache Reuse Optimizations in TensorRT LLM ➡️ link
• [2025/01/14] 📣 Bing's Transition to LLM/SLM Models: Optimizing Search with TensorRT LLM ➡️ link
• [2025/01/04] ⚡Boost Llama 3.3 70B Inference Throughput 3x with TensorRT LLM Speculative Decoding

➡️ link

• [2024/12/10] ⚡ Llama 3.3 70B from AI at Meta is accelerated by TensorRT-LLM. 🌟 State-of-the-art model on par with Llama 3.1 405B for reasoning, math, instruction following and tool use. Explore the preview

➡️ link

• [2024/12/03] 🌟 Boost your AI inference throughput by up to 3.6x. We now support speculative decoding and tripling token throughput with our NVIDIA TensorRT-LLM. Perfect for your generative AI apps. ⚡Learn how in this technical deep dive

➡️ link

• [2024/12/02] Working on deploying ONNX models for performance-critical applications? Try our NVIDIA Nsight Deep Learning Designer ⚡ A user-friendly GUI and tight integration with NVIDIA TensorRT that offers:

✅ Intuitive visualization of ONNX model graphs ✅ Quick tweaking of model architecture and parameters ✅ Detailed performance profiling with either ORT or TensorRT ✅ Easy building of TensorRT engines ➡️ link

• [2024/11/26] 📣 Introducing TensorRT LLM for Jetson AGX Orin, making it even easier to deploy on Jetson AGX Orin with initial support in JetPack 6.1 via the v0.12.0-jetson branch of the TensorRT LLM repo. ✅ Pre-compiled TensorRT LLM wheels & containers for easy integration ✅ Comprehensive guides & docs to get you started

➡️ link

• [2024/11/21] NVIDIA TensorRT LLM Multiblock Attention Boosts Throughput by More Than 3x for Long Sequence Lengths on NVIDIA HGX H200

➡️ link

• [2024/11/19] Llama 3.2 Full-Stack Optimizations Unlock High Performance on NVIDIA GPUs

➡️ link

• [2024/11/09] 🚀🚀🚀 3x Faster AllReduce with NVSwitch and TensorRT LLM MultiShot

➡️ link

• [2024/11/09] ✨ NVIDIA advances the AI ecosystem with the AI model of LG AI Research 🙌

➡️ link

• [2024/11/02] 🌟🌟🌟 NVIDIA and LlamaIndex Developer Contest

🙌 Enter for a chance to win prizes including an NVIDIA® GeForce RTX™ 4080 SUPER GPU, DLI credits, and more🙌 ➡️ link

• [2024/10/28] 🏎️🏎️🏎️ NVIDIA GH200 Superchip Accelerates Inference by 2x in Multiturn Interactions with Llama Models

➡️ link

• [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 #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

• [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 )
• [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 )
• [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 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 an open-sourced library for optimizing LLM and Visual Gen inference. It provides state-of-the-art optimizations, including custom kernels for common inference operations (attention, GEMMs, MoE, ...), algorithmic runtime optimizations (Prefill-Decode disaggregation, Wide Expert Parallelism, Speculative Decoding, etc), and much more, to perform inference efficiently on NVIDIA GPUs.

Architected on PyTorch, TensorRT LLM provides a high-level Python LLM API that supports a wide range of inference setups - from single-GPU to multi-GPU or multi-node deployments. It includes built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA Dynamo and the Triton Inference Server.

TensorRT LLM is designed to be modular and easy to modify. Its PyTorch-native architecture allows developers to experiment with the runtime or extend functionality. Several popular models are also pre-defined and can be customized using native PyTorch code, making it easy to adapt the system to specific needs.

Getting Started

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

• Quick Start Guide
• Running DeepSeek
• Installation Guide
• Supported Hardware, Models, and other Software
• Benchmarking Performance
• Release Notes

Deprecation Policy

Deprecation is used to inform developers that some APIs and tools are no longer recommended for use. Beginning with version 1.0, TensorRT LLM has the following deprecation policy:

1. Com