Mac Mini LLM Lab Agent Skill

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.

2k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

511

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill mac-mini-llm-lab

The instruction itself

19 sections, as written by the author

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 [email protected] 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'
llm.local:443 {
    tls internal
    reverse_proxy localhost:11434

    @api path /v1/*
    handle @api {
        reverse_proxy localhost:11434
    }
}

webui.local:443 {
    tls internal
    reverse_proxy localhost:3000
}
EOF

brew services start caddy

Open WebUI Setup

# Run Open WebUI via Docker
docker run -d \
  --name open-webui \
  -p 3000:8080 \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  -e WEBUI_AUTH=true \
  -v open-webui:/app/backend/data \
  --restart unless-stopped \
  ghcr.io/open-webui/open-webui:main

# Or install Docker first if not available
brew install --cask docker

Monitoring

# Health check script
cat > ~/scripts/llm-health.sh << 'SCRIPT'
#!/bin/bash
# Check Ollama
if curl -sf http://localhost:11434/api/tags > /dev/null; then
    echo "$(date): Ollama OK"
    curl -s http://localhost:11434/api/ps | python3 -m json.tool
else
    echo "$(date): Ollama DOWN"
    # Restart
    launchctl kickstart -k gui/$(id -u)/com.ollama.serve
fi

# System stats
echo "CPU: $(top -l 1 -n 0 | grep 'CPU usage')"
echo "Memory: $(vm_stat | head -5)"
echo "Disk: $(df -h / | tail -1)"
echo "Thermal: $(sudo powermetrics --samplers smc -n 1 2>/dev/null | grep 'CPU die' || echo 'N/A')"
SCRIPT
chmod +x ~/scripts/llm-health.sh

# Schedule health check every 5 minutes
# Add to crontab: crontab -e
# */5 * * * * ~/scripts/llm-health.sh >> ~/logs/llm-health.log 2>&1

Memory Usage by Model

|-------|-------------|-----------------|-----------------|

| llama3.1:8b (Q4) | ~5 GB | ~25 t/s | ~45 t/s |

| qwen2.5:14b (Q4) | ~9 GB | ~15 t/s | ~30 t/s |

| llama3.1:70b (Q4) | ~40 GB | ~5 t/s | ~10 t/s |

| nomic-embed-text | ~300 MB | N/A | N/A |

| codellama:13b | ~8 GB | ~18 t/s | ~35 t/s |

Security Checklist

# Enable FileVault disk encryption
sudo fdesetup enable

# Enable firewall
sudo /usr/libexec/ApplicationFirewall/socketfilterfw --setglobalstate on
sudo /usr/libexec/ApplicationFirewall/socketfilterfw --setstealthmode on

# Disable unnecessary sharing services
sudo launchctl disable system/com.apple.screensharing
sudo launchctl disable system/com.apple.AirPlayXPCHelper

# Set strong admin password
# System Settings > Users & Groups

# Restrict Ollama to local network only (if not using Tailscale)
# Set OLLAMA_HOST=127.0.0.1 in launchd plist

Performance Tuning

# Increase file descriptor limits for concurrent requests
sudo launchctl limit maxfiles 65536 200000

# Check unified memory pressure
memory_pressure

# Monitor GPU usage (Metal)
sudo powermetrics --samplers gpu_power -n 1

# Optimize for inference (disable Spotlight indexing on model dirs)
mdutil -i off ~/.ollama

Troubleshooting

| Issue | Solution |

|-------|---------|

| Model loading slow | First load caches to memory; subsequent loads are fast |

| Out of memory | Use smaller quantization (Q4_K_M), reduce OLLAMA_MAX_LOADED_MODELS |

| Mac sleeping | Run sudo pmset -a disablesleep 1 |

| Ollama not starting | Check launchctl list | grep ollama, view /tmp/ollama.err |

| Slow over Wi-Fi | Use Ethernet; Wi-Fi adds latency to streaming responses |

| Thermal throttling | Ensure adequate ventilation, check powermetrics |

ollama-stack — Software stack with Docker Compose and LiteLLM
ssh-configuration — Secure remote access
vpn-setup — Remote access via WireGuard/Tailscale

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How to use it

Copy the folder

Take bagelhole/mac-mini-llm-lab from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip, uv, brew, docker. Without those the skill loads but fails at the first command.

Mac Mini LLM Lab Agent Skill

Install

The instruction itself

Mac mini LLM Lab

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)

SSH Hardening

Reverse Proxy with Caddy

Open WebUI Setup

Monitoring

Memory Usage by Model

Security Checklist

Performance Tuning

Troubleshooting

Related Skills

Other skills for the same job

How to use it