Skill: twinsgeeks/mac-mini-ai (version 1.0.1)

--- name: mac-mini-ai description: Mac Mini AI — run LLMs, image generation, speech-to-text, and embeddings on your Mac Mini. M4 (16-32GB) and M4 Pro (24-64GB) configurations make the Mac Mini the most affordable entry point for local AI. Stack multiple Mac Minis into a fleet for the cost of one cloud GPU. Route requests across all your Mac Minis automatically. version: 1.0.0 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"computer","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin"]}} --- # Mac Mini AI — The $599 AI Node The Mac Mini is the most cost-effective hardware for local AI. Starting at $599 with 16GB of unified memory, it runs 7B-14B models comfortably. Stack three Mac Minis for the cost of one month of cloud GPU rental — and they run forever with zero ongoing costs. This skill turns one Mac Mini into an AI server and multiple Mac Minis into a fleet. ## Mac Mini configurations for AI | Config | Chip | Unified Memory | Price | LLM Sweet Spot | |--------|------|---------------|-------|----------------| | Mac Mini M4 (16GB) | M4 | 16GB | $599 | 3B-7B models (`phi4-mini`, `llama3.2:3b`) | | Mac Mini M4 (24GB) | M4 | 24GB | $799 | 7B-14B models (`phi4`, `gemma3:12b`) | | Mac Mini M4 (32GB) | M4 | 32GB | $999 | 14B-22B models (`qwen3:14b`, `codestral`) | | Mac Mini M4 Pro (48GB) | M4 Pro | 48GB | $1,399 | 22B-32B models (`qwen3:32b`) | | Mac Mini M4 Pro (64GB) | M4 Pro | 64GB | $1,799 | 32B-70B models (`llama3.3:70b` quantized) | ## The Mac Mini fleet strategy Three Mac Minis (32GB each) for $3,000 give you: - 96GB total unified memory across the fleet - Each runs a different model simultaneously - The router picks the best device for every request - $0/month after purchase — no cloud API costs ``` Mac Mini #1 (32GB) — llama3.3:70b (quantized) ─┐ Mac Mini #2 (32GB) — codestral + phi4 ├──→ Router ←── Your apps Mac Mini #3 (32GB) — qwen3:14b + embeddings ─┘ ``` ## Setup ```bash pip install ollama-herd # PyPI: https://pypi.org/project/ollama-herd/ ``` **On one Mac Mini (the router):** ```bash herd ``` **On every other Mac Mini:** ```bash herd-node ``` Devices discover each other automatically. No IP configuration, no Docker, no Kubernetes. ## Use your Mac Mini ### Chat with an LLM ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") response = client.chat.completions.create( model="phi4", messages=[{"role": "user", "content": "Write a Python web scraper"}], stream=True, ) for chunk in response: print(chunk.choices[0].delta.content or "", end="") ``` ### Ollama API ```bash curl http://localhost:11435/api/chat -d '{ "model": "gemma3:12b", "messages": [{"role": "user", "content": "Explain recursion simply"}], "stream": false }' ``` ### Image generation (optional) ```bash uv tool install mflux # Install on any Mac Mini curl -o art.png http://localhost:11435/api/generate-image \ -H "Content-Type: application/json" \ -d '{"model": "z-image-turbo", "prompt": "a stack of Mac Minis glowing", "width": 512, "height": 512}' ``` ### Speech-to-text ```bash curl http://localhost:11435/api/transcribe -F "file=@meeting.wav" -F "model=qwen3-asr" ``` ### Embeddings for RAG ```bash curl http://localhost:11435/api/embed \ -d '{"model": "nomic-embed-text", "input": "Mac Mini home server local AI"}' ``` ## Best models for Mac Mini | RAM | Best models | Why | |-----|------------|-----| | 16GB | `phi4-mini` (3.8B), `gemma3:4b`, `nomic-embed-text` | Small but capable, leaves room for OS | | 24GB | `phi4` (14B), `gemma3:12b`, `codestral` | Sweet spot for single-model use | | 32GB | `qwen3:14b`, `deepseek-r1:14b`, `codestral` + `phi4-mini` | Two models simultaneously | | 48GB | `qwen3:32b`, `deepseek-r1:32b` | Larger models, great quality | | 64GB | `llama3.3:70b` (quantized) | Near-frontier quality on a Mac Mini | ## Monitor your Mac Mini fleet Dashboard at `http://localhost:11435/dashboard` — see every Mac Mini's status, loaded models, and queue depths. ```bash # Fleet overview curl -s http://localhost:11435/fleet/status | python3 -m json.tool # Model recommendations for your hardware curl -s http://localhost:11435/dashboard/api/recommendations | python3 -m json.tool ``` ## Works with any OpenAI-compatible tool | Tool | Connection | |------|-----------| | **Open WebUI** | Ollama URL: `http://mac-mini-ip:11435` | | **Aider** | `aider --openai-api-base http://mac-mini-ip:11435/v1` | | **Continue.dev** | Base URL: `http://mac-mini-ip:11435/v1` | | **LangChain** | `ChatOpenAI(base_url="http://mac-mini-ip:11435/v1")` | ## Full documentation - [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) - [API Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/api-reference.md) - [Configuration](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/configuration-reference.md) ## Contribute Ollama Herd is open source (MIT). Built for the Mac Mini fleet community: - [Star on GitHub](https://github.com/geeks-accelerator/ollama-herd) — help other Mac Mini owners find us - [Open an issue](https://github.com/geeks-accelerator/ollama-herd/issues) — share your Mac Mini fleet setup - **PRs welcome** from humans and AI agents. `CLAUDE.md` gives full context. - Running a Mac Mini cluster? We'd love to hear about it. ## Guardrails - **No automatic downloads** — model pulls require explicit user confirmation. - **Model deletion requires explicit user confirmation.** - **All requests stay local** — no data leaves your network. - Never delete or modify files in `~/.fleet-manager/`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)