mac-mini-llm-lab
Helps your agent set up a local AI server. It allows running large language models privately and always on. Useful for having your own AI without relying on external services.
What it does
Turns a Mac mini into a local artificial intelligence (AI) server. It allows you to run powerful AI models directly on your computer, with access from anywhere and monitoring.
When to use it
- ✓When you want to create your own AI at home.
- ✓To develop AI without relying on the cloud.
- ✓To run models that require a lot of computer memory.
Example of what to ask your AI
“Set up my Mac mini to run AI locally.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
This guide teaches you how to install programs on the Mac mini that allow AI models to run. It also sets up remote access and monitoring to ensure everything works well.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill mac-mini-llm-labOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/mac-mini-llm-lab/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Mac mini LLM Lab
Turn a Mac mini into a low-noise, always-on local AI appliance.
When to Use This Skill
Use this skill when:
- Setting up a dedicated local LLM inference server
- Building a private AI development environment
- Need always-on model serving without cloud costs
- Running models that require Apple Silicon unified memory (32-192GB)
- Creating a home lab AI server for a small team
Prerequisites
- Mac mini with Apple Silicon (M2/M3/M4, 16GB+ unified memory recommended)
- macOS Sonoma 14+ or Sequoia 15+
- Ethernet connection (recommended over Wi-Fi)
- UPS for power protection (optional but recommended)
Initial System Setup
# Update macOS
softwareupdate --install --all
# Install Xcode command-line tools
xcode-select --install
# Install Homebrew
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Core packages
brew install tmux htop btop wget jq git neovim
# Python environment (for MLX and custom scripts)
brew install python@3.12 uv
# Monitoring
brew install prometheus node_exporter
Ollama Setup
# Install Ollama
brew install ollama
# Pull models based on your RAM
# 16GB Mac mini:
ollama pull llama3.1:8b
ollama pull nomic-embed-text
ollama pull codellama:7b
# 32GB Mac mini:
ollama pull llama3.1:8b
ollama pull qwen2.5:14b
ollama pull deepseek-coder-v2:16b
ollama pull nomic-embed-text
# 64GB+ Mac mini:
ollama pull llama3.1:70b
ollama pull qwen2.5:32b
ollama pull codellama:34b
# Verify Metal acceleration
ollama run llama3.1:8b --verbose
# Look for: "metal" in output
MLX Framework (Apple Silicon Native)
MLX runs models natively on Apple Silicon with excellent performance:
# Install MLX
uv pip install mlx mlx-lm
# Run a model
python3 -c "
from mlx_lm import load, generate
model, tokenizer = load('mlx-community/Llama-3.1-8B-Instruct-4bit')
response = generate(model, tokenizer, prompt='Explain Docker in 3 sentences', max_tokens=200)
print(response)
"
# MLX server (OpenAI-compatible API)
uv pip install mlx-lm[server]
mlx_lm.server --model mlx-community/Llama-3.1-8B-Instruct-4bit --port 8080
Auto-Start with launchd
<!-- ~/Library/LaunchAgents/com.ollama.serve.plist -->
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>com.ollama.serve</string>
<key>ProgramArguments</key>
<array>
<string>/opt/homebrew/bin/ollama</string>
<string>serve</string>
</array>
<key>EnvironmentVariables</key>
<dict>
<key>OLLAMA_HOST</key>
<string>0.0.0.0</string>
<key>OLLAMA_NUM_PARALLEL</key>
<string>4</string>
<key>OLLAMA_MAX_LOADED_MODELS</key>
<string>2</string>
<key>OLLAMA_FLASH_ATTENTION</key>
<string>1</string>
</dict>
<key>RunAtLoad</key>
<true/>
<key>KeepAlive</key>
<true/>
<key>StandardOutPath</key>
<string>/tmp/ollama.log</string>
<key>StandardErrorPath</key>
<string>/tmp/ollama.err</string>
</dict>
</plist>
# Load the service
launchctl load ~/Library/LaunchAgents/com.ollama.serve.plist
# Check status
launchctl list | grep ollama
# Unload if needed
launchctl unload ~/Library/LaunchAgents/com.ollama.serve.plist
Power & Reliability
# Prevent sleep (keeps running with lid closed on Mac mini)
sudo pmset -a disablesleep 1
sudo pmset -a sleep 0
# Auto-restart after power failure
sudo pmset -a autorestart 1
# Schedule weekly reboot (Sunday 4 AM)
sudo pmset repeat shutdown MTWRFSU 03:55:00
sudo pmset repeat poweron MTWRFSU 04:00:00
# Check power settings
pmset -g
Remote Access
Tailscale (Recommended)
# Install Tailscale for easy secure remote access
brew install --cask tailscale
# Enable from menu bar, authenticate
# Access your Mac mini from anywhere: http://mac-mini:11434
SSH Hardening
# Enable remote login
sudo systemsetup -setremotelogin on
# Edit SSH config
sudo nano /etc/ssh/sshd_config
# Add:
# PasswordAuthentication no
# PubkeyAuthentication yes
# PermitRootLogin no
# AllowUsers yourusername
# Restart SSH
sudo launchctl unload /System/Library/LaunchDaemons/ssh.plist
sudo launchctl load /System/Library/LaunchDaemons/ssh.plist
Reverse Proxy with Caddy
brew install caddy
# Caddyfile
cat > /opt/homebrew/etc/Caddyfile << 'EOF'
…
FAQ
Do I need a new Mac mini?
A Mac mini with an Apple Silicon chip (M2 or newer) and at least 16GB of memory is recommended for good performance.
Can I access it from another computer?
Yes, the guide shows how to set up secure internet access using tools like Tailscale or SSH.
What are 'AI models'?
These are the programs that give AI its 'thinking' ability. Examples include models for generating text, code, or understanding images.
Details
Original description by the author
Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation. Use when building an always-on private AI inference server on Apple Silicon.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3511
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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