sickn33/agentic-awesome-skills/mac-mini-llm-lab
Claude Code and agent skill by sickn33/agentic-awesome-skills
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Configure a Mac mini as a reliable local LLM server with remote access,
Install
npx vskill@latest install sickn33/agentic-awesome-skills/mac-mini-llm-labSKILL.md
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 fit the actual machine’s unified memory
- Creating a home lab AI server for a small team
Prerequisites
- An actual Apple Silicon Mac mini configuration with sufficient available memory for the selected model
- macOS Sonoma 14+ or Sequoia 15+
- Ethernet connection (recommended over Wi-Fi)
- UPS for power protection (optional but recommended)
Host and exposure boundaries
Check actual model hardware/memory and installed macOS/tool support first. The throughput/RAM table is illustrative, not measured evidence or a fit guarantee; include KV cache/context/concurrency overhead. Keep Ollama loopback-only by default; TLS alone in the Caddy example does not add authentication. Configure an authenticated private proxy/tunnel and access policy before remote exposure. Loopback binding must be deliberately adapted for Docker access, without exposing an unauthenticated LAN listener. Approve persistent services, downloads, updates, reboots and FileVault/power changes separately as applicable; preserve existing settings and recovery access. Create script/log directories before writing them and do not run privileged diagnostics unattended from cron.
Initial System Setup
Ollama Setup
MLX Framework (Apple Silicon Native)
MLX runs models natively on Apple Silicon with excellent performance:
Auto-Start with launchd
Power & Reliability
Remote Access
Tailscale (Recommended)
SSH Hardening
Reverse Proxy with Caddy
Open WebUI Setup
Monitoring
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
Performance Tuning
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 |
Related Skills
- ollama-stack (
ollama-stack) — Software stack with Docker Compose and LiteLLM - ssh-configuration (
ssh-configuration) — Secure remote access - vpn-setup (
vpn-setup) — Remote access via WireGuard/Tailscale
Limitations
- Infrastructure commands can disrupt services: confirm target host/scope and have backups/snapshots before mutating state.
- Docs-only import: upstream scripts and templates not bundled.
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/mac-mini-llm-lab/SKILL.md
Popularity
- Repository stars
- 47,054
- Repository forks
- 6,853
- 7-day trend score
- 54
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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