awesome-local-llm

🥈Silver

A curated list of platforms, tools, and resources to run LLMs locally. Helps operations teams deploy and manage self-hosted AI models. Connects to inference platforms and engines for integration.

1,521870Updated Yesterday

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/rafska/awesome-local-llm.git

Works with:

Claude

Overview

About This Skill

https://github.com/awesome-local-llm

How to Use

1. Identify the specific LLM model and use case you want to deploy locally. 2. Use the prompt template to request recommendations for platforms, hardware, and tools. 3. Evaluate the recommendations based on your technical expertise and infrastructure. 4. Set up the chosen platform and tools, following the provided guidelines. 5. Monitor the performance and usage of the deployed model to ensure it meets your requirements.

Use Cases

Set up a local LLM for developing custom AI applications without cloud dependency.

Evaluate and compare various LLMs using benchmarks and leaderboards provided in the resources.

Automate the deployment of LLMs on personal hardware for testing and development purposes.

Utilize user-friendly interfaces to streamline interactions with locally hosted AI models.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/rafska/awesome-local-llm

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Recommend the best platforms and tools to run [MODEL_NAME] locally for [USE_CASE]. Include setup requirements, hardware recommendations, and any dependencies. Also suggest monitoring tools to track performance and usage.

Example Output

For running Llama 2 locally for a customer support chatbot, I recommend the following setup:

1. **Platform**: Ollama - It's user-friendly and supports Llama 2 out of the box. You can install it on a Linux server with an NVIDIA GPU.
2. **Hardware**: A server with at least 32GB RAM and an NVIDIA RTX 3090 GPU (24GB VRAM) is recommended for optimal performance.
3. **Dependencies**: Docker and NVIDIA Container Toolkit for containerization and GPU support.
4. **Monitoring**: Use Prometheus and Grafana to monitor GPU usage, memory consumption, and response times. Set up alerts for any anomalies.

Ollama provides a simple CLI to interact with the model, and you can integrate it with your existing support systems via APIs. Make sure to regularly update the model and dependencies to ensure security and performance.