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tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT and quantization.

Updated May 4, 2026
Search Tags:#LLM#GPU#quantization#inference#TensorRT#multi-GPU#NVIDIA
One-click install
npx skills add https://github.com/luokai25/luo-ai-skills-market --skill tensorrt-llm-luokai25
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/luokai25/luo-ai-skills-market/tree/main/09-data-and-ai%20%28by%20Luo%20Kai%29/03-llm-engineering/tensorrt-llm
Command: npx skills add https://github.com/luokai25/luo-ai-skills-market --skill tensorrt-llm-luokai25

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the problem of slow LLM inference by optimizing it using NVIDIA TensorRT on NVIDIA GPUs, enabling high throughput and low latency for production deployments.

Core Features & Use Cases

  • Inference Optimization: Achieve 10-100x faster inference than PyTorch with TensorRT.
  • Quantization: Supports quantization (FP8/INT4) for models with in-flight batching and multi-GPU scaling.
  • Use Case: Ideal for deploying LLM models in production environments that require high performance and efficiency, such as serving models on NVIDIA GPUs (A100/H100) with quantization and multi-GPU scaling.

Quick Start

Use the trtllm-serve command to start a server for the Meta-Llama-3-8B model with TensorRT-LLM.

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 low latency on NVIDIA GPUs?▼

You can optimize LLM inference on NVIDIA GPUs using TensorRT to achieve maximum throughput and minimum latency. This technique supports production deployment with up to 100x faster inference than standard PyTorch.

Can I use quantization and multi-GPU scaling for large language model inference?▼

Yes, TensorRT supports quantization using FP8 or INT4 alongside in-flight batching and multi-GPU scaling. This allows efficient large language model inference on NVIDIA A100 and H100 GPUs for production environments.

What is the best way to deploy Llama models with high throughput using TensorRT?▼

The best way to deploy Llama models with high throughput is using the trtllm-serve command. This starts a server optimized by TensorRT-LLM to accelerate large language model inference on NVIDIA GPUs.

Does TensorRT-LLM require specific Python or CUDA environments to run?▼

Yes, TensorRT-LLM requires CUDA, TensorRT, and Python versions 3.10 through 3.12 to run. It also depends on PyTorch and tensorrt-llm libraries to successfully execute optimized large language model inference.

Why is my PyTorch LLM inference too slow for production serving?▼

Standard PyTorch LLM inference is often too slow for production because it lacks deep GPU optimization. Using TensorRT for inference optimization solves this by enabling 10x to 100x faster execution with in-flight batching.

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