How do I install TensorRT-LLM?

Installation typically requires building from source using provided Docker containers or following the detailed build guide due to its C++/CUDA dependencies.

Which LLMs are supported?

It supports a wide range of models including LLaMA, GPT, Falcon, Mistral, Mixtral, and many others via a generic model definition.

Can I run it on AMD or Intel GPUs?

No, TensorRT-LLM is designed exclusively for the NVIDIA CUDA ecosystem and requires NVIDIA GPUs.

How does the engine build process work?

You define a model architecture in Python, then TensorRT-LLM compiles it into an optimized runtime engine with fused kernels and quantized weights.

What is the 'trtllm-serve' tool?

A command-line utility to quickly launch an inference server for a built TensorRT-LLM engine, integrating with Triton.

How is multi-GPU parallelism handled?

Through tensor parallelism (sharding layers across GPUs) and pipeline parallelism (sharding model stages), configured during engine build.

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TensorRT-LLM

TensorRT-LLM is an open-source library from NVIDIA designed to optimize and accelerate the inference of large language models (LLMs) on NVIDIA GPUs. It provides tools to build highly efficient and optimized inference engines for a wide range of transformer models, including support for state-of-the-art architectures and quantization techniques. Key capabilities include model compilation, kernel fusion, quantization (FP8, INT4, INT8), in-flight batching, tensor parallelism, and pipeline parallelism for distributed inference. It integrates with NVIDIA's Triton Inference Server for production deployment and supports popular model formats. Targeted at AI researchers, ML engineers, and developers building production LLM applications, TensorRT-LLM is essential for maximizing throughput and minimizing latency on NVIDIA hardware.

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Added on

Sep 22, 2026

NVIDIA GPUsLLM InferenceHigh PerformanceMulti-GPUTensorRTOpen Source

Product information

Everything worth knowing about TensorRT-LLM

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Optimized LLM Inference

Delivers high-performance inference for large language models on NVIDIA GPUs through optimized kernels and runtime.

Multi-GPU & Multi-Node Scaling

Supports tensor parallelism and pipeline parallelism to scale inference across multiple GPUs and nodes.

Advanced Quantization & Compression

Implements techniques like INT8/FP8 quantization, weight-only quantization, and sparsity to reduce memory footprint and boost throughput.

In-Flight Batching & Continuous Batching

Dynamically batches requests for efficient GPU utilization and high throughput under concurrent load.

Technical capabilities

TensorRT Engine Builder

Automatically builds optimized TensorRT engines from model checkpoints with graph-level and kernel-level optimizations.

Python & C++ APIs

Provides both high-level Python APIs for ease of use and low-level C++ APIs for maximum performance and control.

Triton Inference Server Integration

Includes a backend for seamless deployment with NVIDIA Triton Inference Server for production serving.

Must watch videos

TensorRT LLM 1.0 Livestream: New Easy-To-Use Pythonic Runtime

31:35

Tutorial31:35

TensorRT LLM 1.0 Livestream: New Easy-To-Use Pythonic Runtime

TensorRT LLM v1.0 includes a new architecture build with modular Python and PyTorch to make deployment and development easy, all while still delivering leading LLM inference performance on NVIDIA systems. Links and pages to learn more about TensorRT LLM https://nvidia.github.io/TensorRT-LLM/ https://github.com/NVIDIA/TensorRT-LLM Release notes: https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/release-notes.html Quick Start Guide https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html Docs https://docs.nvidia.com/tensorrt-llm/index.html

What is Pytorch, TF, TFLite, TensorRT, ONNX?

3:57

Tutorial3:57

What is Pytorch, TF, TFLite, TensorRT, ONNX?

Basic ideas behind Pytorch, TF, TFLite, TensorRT, ONNX in machine learning

15:14

Tutorial15:14

Why Inference is hard..

Follow me: X: https://x.com/calebfoundry LinkedIn: https://www.linkedin.com/in/calebeom/ TikTok: https://www.tiktok.com/@calebwritescode Inference requires efficient loading and quantization of the model. This video covers the depth and breadth of various methods when it comes to loading and quantization like mmap, standard quantization, GGUF, AWQ, EXL2, FP8, and NVFP4. We also get into various inference engines like llama.cpp, vLLM, SGLang, TensorRT-LLM, and TGI - though the difference here will be accentuated more as we talk about pre-fill, decoding, and serving the model for concurrency and scheduling. #inference #deeplearning #llm Zo Computer: https://zo.computer Chapters 00:00 Intro 01:14 Artifacts 02:46 Load 03:30 mmap 05:52 Sponsor: Zo 06:38 Quantization 07:43 Standard 09:52 GGUF 11:51 AWQ 13:05 EXL2 14:19 FP8, NVFP4 14:42 Conclusion

Who uses it

Real use cases, no hype

High-Throughput Inference

Serve LLMs at scale with maximum tokens/second on NVIDIA GPUs.

Real-Time Chat Applications

Achieve low-latency responses for interactive AI assistants.

Batch Processing Pipelines

Process large datasets of text efficiently with optimized batching.

Edge Deployment (Jetson)

Deploy optimized LLMs on NVIDIA Jetson edge devices.

Multi-GPU Model Serving

Serve models too large for a single GPU using parallelism.

Fine-tuned Model Deployment

Optimize and deploy custom fine-tuned LLMs with quantization.

What's great

Massive performance gains on NVIDIA GPUs
Seamless integration with NVIDIA Triton Inference Server
Support for all major LLM architectures
In-flight batching for high throughput

Technical strengths

Highly optimized custom CUDA kernels
Advanced quantization (FP8, INT4, GPTQ, AWQ)
Multi-GPU tensor and pipeline parallelism

Where it falls short

NVIDIA GPU ecosystem lock-in (no AMD/Intel)
Complex build process and dependencies
Rapidly changing API between versions
Engine build time can be very long

Technical limitations

Steep learning curve for advanced features
Primarily C++/Python stack, not pure Python

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

An open-source library for optimizing and deploying large language models on NVIDIA GPUs for maximum performance.

Traffic Insights

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Stable

Traffic has remained stable.

Pricing overview

Unknown Pricing

TensorRT-LLM Pricing

TensorRT-LLM uses a unknown pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on TensorRT-LLM

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