mac-mini-llm-lab

Mode d’emploi

Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
Consultez le document original pour les outils, identifiants et environnements requis.
Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.

npx skills add https://github.com/bagelhole/devops-security-agent-skills --skill mac-mini-llm-lab

Avant de commencer

L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.

Document Skill original

Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.

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

SSH Hardening

bash

# 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

bash

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

bash

# 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

bash

# 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

Model	RAM Required	Tokens/sec (M2)	Tokens/sec (M4)
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

bash

# 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

bash

# 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 (Q4KM), 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`

Related Skills

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

Mode d’emploi

Avant de commencer

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

Tâches connexes

Autres Skills

linux-administration

performance-tuning

agent-evals

ai-agent-security

mac-mini-llm-lab

Mode d’emploi

Avant de commencer

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

Tâches connexes

backend-patterns

setup-matt-pocock-skills

setup-pre-commit

docker-patterns

Autres Skills

linux-administration

performance-tuning

agent-evals

ai-agent-security