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tensorrt-llm

Optimize LLM inference with NVIDIA TensorRT for high throughput and low latency.

Updated Jun 17, 2026
Search Tags:#optimization#llm#gpu#inference#fp8#tensorrt#multi-gpu
One-click install
npx skills add https://github.com/anilcan-kara/nozich-agent --skill tensorrt-llm-anilcan-kara
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/anilcan-kara/nozich-agent/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/anilcan-kara/nozich-agent --skill tensorrt-llm-anilcan-kara

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Optimizes production LLM inference by leveraging NVIDIA TensorRT to achieve higher throughput and lower latency.

Core Features & Use Cases

  • High-throughput inference: Accelerates models with FP8/INT4 quantization and multi-GPU setups.
  • Production-grade serving: Seamless integration with trtllm-serve and OpenAI-like APIs for scalable deployments.
  • Versatile deployment options: Supports single-node and multi-node configurations across NVIDIA GPUs.

Quick Start

Start a production deployment of your LLM using TensorRT-LLM and verify throughput improvements.

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 lower latency on NVIDIA GPUs?▼

TensorRT maximizes LLM inference throughput by applying FP8 or INT4 quantization and in-flight batching. It minimizes latency during production deployments on NVIDIA GPUs using multi-GPU setups.

Can I use multi-GPU setups for high-throughput LLM serving?▼

Yes, high-throughput LLM serving supports multi-GPU setups. You can configure single-node and multi-node deployments across NVIDIA GPUs to scale production-grade inference.

Do I need PyTorch and a CUDA environment to run TensorRT-LLM optimization?▼

Yes, you need PyTorch, the tensorrt-llm package, and a compatible CUDA environment to configure and run optimized LLM serving. These dependencies are required to execute the optimization process.

What is the best way to deploy an LLM with FP8 quantization in production?▼

Deploy FP8 quantized LLMs in production using trtllm-serve. It integrates with OpenAI-like APIs to accelerate models and ensure scalable, high-throughput serving across NVIDIA GPUs.

Does TensorRT-LLM support OpenAI-like APIs for scalable deployments?▼

Yes, TensorRT-LLM supports OpenAI-like APIs for scalable deployments. It integrates with trtllm-serve to provide production-grade serving for optimized large language models.

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