Mayur Rathi

⭐ 47.3k GitHub stars

mac-mini-llm-lab

mac-mini-llm-lab is an engineering AI skill with a core value of Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation. It helps developers solve real-world problems in the engineering domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

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.

Last verified on: 2026-10-06

Quick Facts

Category engineering

Works With Claude

Source sickn33/antigravity-awesome-skills

Stars ⭐ 47.3k

Last Verified 2026-10-06

Risk Level Low

mkdir -p ./skills/mac-mini-llm-lab && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/mac-mini-llm-lab/SKILL.md -o ./skills/mac-mini-llm-lab/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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

bash

# 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

bash

# 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:

bash

# 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

xml

<!-- ~/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>

bash

# 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

bash

# 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)

bash

# 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

🎯 Best For

UI designers
Product designers
Claude users
AI users

💡 Use Cases

Generating component mockups
Creating design system tokens
Using mac-mini-llm-lab in daily workflow
Automating repetitive engineering tasks

📖 How to Use This Skill

1
Install the Skill

Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
2
Load into Your AI Assistant

Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
3
Apply mac-mini-llm-lab to Your Work

Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
4
Review and Refine

Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does this work with Figma?

Some design skills integrate with Figma plugins. Check the Works With section for supported tools.

How do I install mac-mini-llm-lab?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/mac-mini-llm-lab/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Skipping usability testing

AI-generated designs should be validated with real users before development.

Not reading the full skill

Skills contain important context and edge cases beyond the quick start.

🔗 Related Skills

aegisops-ai aegisops-ai agent-evals agent-evals agent-observability agent-observability ai-agent-evaluation-benchmarking ai-agent-evaluation-benchmarking ai-agent-tool-routing ai-agent-tool-routing ai-code-generation-guardrails ai-code-generation-guardrails

mac-mini-llm-lab

Quick Facts

Skill Content

When to Use This Skill

Prerequisites

Initial System Setup

Ollama Setup

MLX Framework (Apple Silicon Native)

Auto-Start with launchd

Power & Reliability

Remote Access

Tailscale (Recommended)

🎯 Best For

💡 Use Cases

📖 How to Use This Skill

Install the Skill

Load into Your AI Assistant

Apply mac-mini-llm-lab to Your Work

Review and Refine

❓ Frequently Asked Questions

Does this work with Figma?

How do I install mac-mini-llm-lab?

Can I customize this skill for my team?

⚠️ Common Mistakes to Avoid

Skipping usability testing

Not reading the full skill

🔗 Related Skills