tensorrt-llm

Optimize LLM inference with NVIDIA TensorRT on NVIDIA GPUs.

Updated May 8, 2026

Search Tags:#llm #low latency #nvidia #inference optimization #high throughput #tensorrt

Ready to run

npx skills add https://github.com/superfhp/lumi-agent --skill tensorrt-llm-superfhp

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/superfhp/lumi-agent/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/superfhp/lumi-agent --skill tensorrt-llm-superfhp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tensorrt, torch, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of optimizing Large Language Model (LLM) inference with NVIDIA TensorRT, enabling maximum throughput and lowest latency for production deployment on NVIDIA GPUs.

Core Features & Use Cases

Optimized Inference: Achieve 10-100x faster inference than PyTorch on NVIDIA GPUs.
Quantization Support: Serve models with quantization (FP8/INT4) for efficiency.
In-Flight Batching: Dynamic batching during generation for increased throughput.
Multi-GPU Scaling: Scale across multiple GPUs or nodes for high-performance inference.
Use Case: Ideal for deploying LLMs in production environments where speed and efficiency are critical, such as in AI applications, data centers, and cloud services.

Quick Start

Use the tensorrt-llm skill to optimize the inference of the Meta-Llama-3-8B model on an NVIDIA A100 GPU.

Frequently Asked Questions about tensorrt-llm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize LLM inference for high throughput and low latency on NVIDIA GPUs?▼

You can optimize LLM inference using NVIDIA TensorRT, which applies in-flight batching and quantization to achieve 10-100x faster inference than PyTorch on NVIDIA GPUs.

Does TensorRT support quantization for large language models during production deployment?▼

Yes, TensorRT supports serving models with quantization, including FP8 and INT4 formats, to maximize efficiency and reduce latency for large language models in production environments.

What's the best way to scale LLM inference across multiple GPUs or nodes?▼

The best way to scale LLM inference across multiple GPUs or nodes is using TensorRT's multi-GPU scaling capabilities, which are designed for high-performance model serving in data centers.

Do I need PyTorch and TensorRT installed to optimize model inference?▼

Yes, you need PyTorch and TensorRT installed, along with CUDA and Python libraries, to optimize and deploy large language models for high-performance AI inference on NVIDIA GPUs.

Why does TensorRT provide faster inference than PyTorch for large language models?▼

TensorRT provides faster inference than PyTorch by utilizing in-flight batching during generation, multi-GPU scaling, and quantization support, achieving 10-100x speed improvements for production deployment.

Can I use TensorRT to optimize Llama models for production serving?▼

Yes, you can use TensorRT to optimize LLM inference for models like Meta-Llama-3-8B on NVIDIA A100 GPUs, providing high throughput and low latency for production serving.