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tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT-LLM quantization and batching.

3|Updated Feb 21, 2026
Search Tags:#production deployment#quantization#nvidia#gpu acceleration#llm inference#tensorrt-llm
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill tensorrt-llm-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/12-inference-serving/tensorrt-llm
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill tensorrt-llm-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill optimizes Large Language Model (LLM) inference for production environments on NVIDIA GPUs, drastically reducing latency and increasing throughput compared to standard frameworks.

Core Features & Use Cases

  • High-Performance Inference: Achieves 10-100x faster inference speeds on NVIDIA A100/H100/GB200 GPUs.
  • Production Deployment: Ideal for serving LLMs with features like quantization (FP8, INT4), in-flight batching, and multi-GPU scaling.
  • Use Case: Deploying a Llama 3-70B model for a real-time chatbot application where sub-100ms response times are critical, leveraging FP8 quantization and in-flight batching for maximum efficiency.

Quick Start

Use the tensorrt-llm skill to serve the meta-llama/Meta-Llama-3-8B model with tensor parallelism across 4 GPUs.

Frequently Asked Questions about tensorrt-llm

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

FAQPage Schema
How do I accelerate LLM inference on NVIDIA GPUs for production deployment?▼

To accelerate LLM inference on NVIDIA GPUs for production deployment, use TensorRT-LLM to achieve 10-100x faster speeds through FP8/INT4 quantization, in-flight batching, and multi-GPU scaling.

Can I use FP8 or INT4 quantization to reduce latency for large language models?▼

Yes, you can use FP8 or INT4 quantization to reduce latency for large language models, enabling sub-100ms response times and high throughput by optimizing tensor parallelism across multiple GPUs.

Does TensorRT-LLM support multi-GPU scaling across A100 and H100 hardware?▼

Yes, TensorRT-LLM supports multi-GPU scaling across A100, H100, and GB200 hardware, utilizing tensor parallelism to distribute large model inference workloads efficiently for production deployment.

What is in-flight batching and how does it help GPU acceleration?▼

In-flight batching is a production deployment feature that dynamically groups incoming LLM inference requests to maximize GPU acceleration, achieving high throughput and maintaining low latency for real-time chatbot applications.

What is the best way to serve a Llama 3 model with sub-100ms response times?▼

The best way to serve a Llama 3 model with sub-100ms response times is applying FP8 quantization and in-flight batching via TensorRT-LLM, utilizing tensor parallelism across 4 GPUs for production deployment.

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