skills.rest
NITISH-gitbit
NITISH-gitbitCOMMUNITY
Explore all skills by this author →
GitHub

tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT and FP8/INT4 quantization.

Updated Jun 26, 2026
Search Tags:#GPU acceleration#LLM#quantization#low latency#inference optimization#high throughput#NVIDIA TensorRT
One-click install
npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill tensorrt-llm-nitish-gitbit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill tensorrt-llm-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enhances LLM inference by utilizing NVIDIA TensorRT to significantly improve throughput and reduce latency on NVIDIA GPUs.

Core Features & Use Cases

  • Inference Optimization: Optimizes inference performance with NVIDIA TensorRT, delivering 10-100x faster processing compared to PyTorch.
  • Quantization Support: Handles quantized models with FP8 and INT4, supporting models like Meta-Llama 3 with high-speed and reduced memory footprint.
  • Batching & Multi-GPU Scaling: Supports dynamic batching and scaling across multiple GPUs, making it suitable for real-time applications and high-load environments.
  • Use Case: For developers who require high-throughput LLM inference in production on NVIDIA GPUs, especially for scenarios with batch processing, quantization, and multi-GPU support.

Quick Start

Execute the following command to initialize and run the tensorrt-llm skill:

docker pull nvidia/tensorrt_llm:latest

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 and reduce latency on NVIDIA GPUs?▼

You can accelerate LLM inference using NVIDIA TensorRT optimization, which delivers 10-100x faster processing compared to PyTorch by targeting high-speed deployment on NVIDIA A100 and H100 GPUs.

Does TensorRT LLM support quantized models like FP8 and INT4?▼

Yes, TensorRT LLM supports quantized models using FP8 and INT4 precision, which significantly reduces the memory footprint while maintaining high-speed inference for models like Meta-Llama 3.

What's the best way to scale high-throughput LLM inference across multiple GPUs?▼

The best way to scale high-throughput LLM inference is using TensorRT's dynamic batching and multi-GPU scaling features, specifically designed for real-time applications and high-load production environments.

Do I need specific CUDA and Python versions to set up TensorRT for LLM optimization?▼

Yes, setting up TensorRT for LLM optimization requires CUDA 13.0.0, TensorRT 10.13.2, and Python 3.10-3.12. You can initialize the environment by pulling the nvidia/tensorrt_llm Docker container.

Why use TensorRT over PyTorch for LLM inference?▼

TensorRT optimizes LLM inference to be 10-100x faster than PyTorch by leveraging NVIDIA GPU hardware acceleration, dynamic batching, and low-precision quantization to maximize throughput and minimize latency.

Related Skills

Explore More Software Engineering Skills→
luokai25luokai25

tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT and quantization.

Community
Advanced
nadicodeainadicodeai

tensorrt-llm

Deploy high-throughput LLM inference on NVIDIA GPUs with tensorrt-llm.

Official
Advanced
jayll1303jayll1303
17

tensorrt-llm

Convert HuggingFace checkpoints into optimized TensorRT-LLM engines with quantization.

Community
Advanced
SignmanalSignmanal
1

llama-cpp

Run local GGUF model inference with llama.cpp across CPU and GPU hardware.

Community
Intermediate