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tensorrt-llm

Optimizes LLM inference throughput and latency on NVIDIA GPUs using TensorRT-LLM with quantization and multi-GPU deployment.

Updated Apr 21, 2026
Search Tags:#production#optimization#llm#gpu#inference#multi-node#tensorrt
One-click install
npx skills add https://github.com/samuelmukoti/myai-agent --skill tensorrt-llm-samuelmukoti
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/samuelmukoti/myai-agent/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/samuelmukoti/myai-agent --skill tensorrt-llm-samuelmukoti

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill includes references (resource) components.

What problem does it solve?

NVIDIA TensorRT-LLM accelerates large language model inference to maximize throughput and minimize latency on NVIDIA GPUs for production deployments.

Core Features & Use Cases

  • High-throughput inference: Speeds up LLM serving on A100/H100 with optimized kernels and in-flight batching.
  • Quantization support: Enables FP8 and INT4 workflows to reduce memory and increase throughput.
  • Production-ready serving: Handles model loading, compilation, and scalable deployment with builtโ€‘in metrics and load balancing.

Quick Start

Install the TensorRT-LLM server and start serving your model with trtllm-serve.

Frequently Asked Questions about tensorrt-llm

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

FAQPage Schema
How do I speed up LLM inference on NVIDIA GPUs for production serving?โ–ผ

To speed up LLM inference, use TensorRT-LLM to apply optimized kernels and in-flight batching, maximizing throughput and minimizing latency on A100 and H100 GPUs.

Does TensorRT-LLM support FP8 and INT4 quantization workflows?โ–ผ

Yes, TensorRT-LLM supports FP8 and INT4 quantization workflows, enabling you to reduce memory consumption and further increase throughput during large language model inference.

Can I deploy TensorRT-LLM across multi-GPU and multi-node configurations?โ–ผ

Yes, you can deploy TensorRT-LLM across multi-GPU and multi-node configurations to handle scalable production deployments with built-in metrics and load balancing.

What is in-flight batching and how does it optimize LLM serving?โ–ผ

In-flight batching is a technique used by TensorRT-LLM to optimize LLM serving by dynamically processing multiple requests, which speeds up inference and increases throughput on NVIDIA GPUs.

How do I start serving a model with the TensorRT-LLM server?โ–ผ

To start serving a model, install the TensorRT-LLM server and use the trtllm-serve command to handle model loading, compilation, and scalable deployment.

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