tensorrt-llm

Optimize large language model inference on NVIDIA GPUs with quantization and dynamic batching.

Updated Jun 25, 2026

Search Tags:#deployment #optimization #llm #gpu #inference #nvidia #tensorrt

Ready to run

npx skills add https://github.com/Rheasilvia/hermes-desktop --skill tensorrt-llm-rheasilvia

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill tensorrt-llm-rheasilvia

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the challenge of deploying large language models with high throughput and low latency, specifically for production environments requiring optimized performance on NVIDIA hardware.

Core Features & Use Cases

Performance Optimization: Implements advanced techniques like FP8 quantization, in-flight batching, and paged KV cache to significantly boost inference speed.
Multi-GPU Scaling: Provides robust support for tensor, pipeline, and expert parallelism to handle massive models across multiple GPUs and nodes.
Use Case: Deploying a Llama 3-70B model in a production environment where you need to serve thousands of concurrent requests with minimal latency and maximum hardware utilization.

Quick Start

Use the tensorrt-llm skill to serve the meta-llama/Meta-Llama-3-8B model with automatic optimization enabled.

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 high-throughput and low-latency serving on NVIDIA GPUs?▼

To optimize LLM inference on NVIDIA GPUs, you can use techniques like FP8 quantization, in-flight batching, and paged KV cache. These methods significantly boost inference speed and maximize hardware utilization for production environments.

What is the best way to deploy a large language model across multiple GPUs and nodes?▼

Deploying large models across multiple GPUs requires tensor, pipeline, and expert parallelism. This approach handles massive models by distributing the computational workload across multiple nodes and GPUs for robust scaling.

How do I serve thousands of concurrent LLM requests with minimal latency?▼

Serve thousands of concurrent LLM requests with minimal latency by using in-flight batching and paged KV cache. This configuration maximizes hardware utilization and maintains high throughput for production-grade AI applications.

Does TensorRT-LLM support an OpenAI-compatible API for production deployment?▼

Yes, TensorRT-LLM supports serving models through an OpenAI-compatible API. This allows you to integrate optimized, CUDA-accelerated inference into existing production workflows seamlessly without changing client application logic.

Can I use FP8 quantization to reduce memory usage during LLM inference?▼

Yes, you can use FP8 quantization to reduce memory usage during LLM inference. Combined with memory-efficient KV caching, it significantly lowers the memory footprint while maintaining high throughput on NVIDIA hardware.