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tensorrt-llm

Optimize and deploy LLM inference on NVIDIA GPUs with TensorRT-LLM.

150|25|Updated Apr 20, 2026
Search Tags:#llm#quantization#inference#gpu-acceleration#serving#tensorrt-llm#multigpu
One-click install
npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill tensorrt-llm-devsoul2026
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/Devsoul2026/Hermes-One-Click/tree/main/hermes-agent/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill tensorrt-llm-devsoul2026

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

NVIDIA GPUs powered LLM inference often underperforms due to CPU bottlenecks or suboptimal runtime. TensorRT-LLM accelerates inference and provides production-grade serving for large language models.

Core Features & Use Cases

  • High-throughput, low-latency inference on NVIDIA GPUs using TensorRT-LLM.
  • Production-grade serving with OpenAI-compatible API and Prometheus metrics.
  • Scalable deployment across single-node and multi-node GPU clusters with various parallelism strategies.
  • Use cases include real-time chat assistants, document QA, and large-scale batch inference.

Quick Start

Start serving by launching trtllm-serve with a model and configured GPUs, then send chat or completions requests.

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 serving?▼

You can accelerate LLM inference on NVIDIA GPUs using TensorRT-LLM to optimize runtime, reduce CPU bottlenecks, and provide production-grade serving with an OpenAI-compatible API.

How do I deploy large language models across multi-node GPU clusters?▼

Deploy large language models across multi-node GPU clusters by configuring tensor, pipeline, and expert parallelism strategies within TensorRT-LLM to achieve scalable, high-throughput inference.

Can I use FP8 and INT4 quantization to optimize LLM inference?▼

Yes, you can apply FP8 and INT4 quantization to optimize LLM inference, reducing memory footprint and increasing throughput while maintaining accuracy on supported NVIDIA GPUs.

Do I need torch and tensorrt-llm dependencies to serve models with an OpenAI-compatible API?▼

Yes, you need both torch and tensorrt-llm dependencies installed to configure the runtime environment and serve models through the ready-made OpenAI-compatible API.

What is the best way to monitor GPU inference latency and throughput for real-time chat assistants?▼

Monitor GPU inference latency and throughput for real-time chat assistants by leveraging the built-in Prometheus metrics exposed by the TensorRT-LLM serving runtime.

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