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Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon

作者 Twin Geeks · GitHub ↗ · v1.0.1 · MIT-0
darwin ✓ 安全检测通过
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在 OpenClaw 中安装
/install mac-mini-ai
功能描述
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...
使用说明 (SKILL.md)

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

pip install ollama-herd    # PyPI: https://pypi.org/project/ollama-herd/

On one Mac Mini (the router):

herd

On every other Mac Mini:

herd-node

Devices discover each other automatically. No IP configuration, no Docker, no Kubernetes.

Use your Mac Mini

Chat with an LLM

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")
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

curl http://localhost:11435/api/chat -d '{
  "model": "gemma3:12b",
  "messages": [{"role": "user", "content": "Explain recursion simply"}],
  "stream": false
}'

Image generation (optional)

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

curl http://localhost:11435/api/transcribe -F "[email protected]" -F "model=qwen3-asr"

Embeddings for RAG

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.

# 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

Contribute

Ollama Herd is open source (MIT). Built for the Mac Mini fleet community:

  • Star on GitHub — help other Mac Mini owners find us
  • Open an issue — 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/.
安全使用建议
This skill is largely coherent with its purpose, but check a few things before installing: 1) The SKILL.md tells you to run 'pip install ollama-herd' — inspect the PyPI package and its GitHub repo (https://github.com/geeks-accelerator/ollama-herd) to ensure the code matches expectations. 2) Confirm how the fleet 'discovery' works (it may open LAN ports or use multicast); if you need to limit network exposure, run in an isolated network or firewall the service. 3) The metadata lists python3/pip as optional even though examples use them — ensure you have a safe Python environment (virtualenv) before installing. 4) The 'uv tool install mflux' command is not explained here — verify what 'uv' is and where that tool comes from. 5) Running the herd will start local servers on port 11435 — review the service config and keep model downloads/installs manual as recommended. If these checks look good, the skill appears to do what it claims; otherwise treat it cautiously and inspect the code before installing.
能力评估
Purpose & Capability
The skill is an instruction-only guide for running a local fleet (herd/herd-node) and references installing a PyPI package (ollama-herd), running local HTTP endpoints, and using local model tooling — which matches the stated purpose. However, metadata marks python3/pip as optional while SKILL.md explicitly instructs 'pip install ollama-herd', so pip should be required; this mismatch is a minor coherence issue.
Instruction Scope
Instructions stick to setting up a local service, using local HTTP endpoints (localhost:11435), and interacting with models. They do not instruct reading unrelated system files or exporting data. Small scope ambiguities: the fleet 'automatic discovery' mechanism is not described (it may perform LAN discovery/multicast or open ports), and the 'uv tool install mflux' command is not explained (the 'uv' tool is undefined here). Also several examples call python3 utilities even though python3/pip were listed optional.
Install Mechanism
This is an instruction-only skill (no install spec), so it doesn't install code itself. It instructs the user to pip install a package from PyPI, which is a normal way to install CLI tools but does execute third-party code on install — users should inspect the package/repo before running. No arbitrary download URLs or archive extraction are embedded in the skill.
Credentials
The skill does not request environment variables or secret credentials and only references local config paths (~/.fleet-manager/*) which are relevant to the fleet. That proportionality is appropriate for the described functionality.
Persistence & Privilege
The skill does not request always:true, does not request system-wide changes in its instructions, and is user-invocable only. The skill starts local services (herd/herd-node) which is expected for this purpose but carries the usual runtime privilege of any local server process.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install mac-mini-ai
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /mac-mini-ai 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
No changes detected in this release. - Version number updated to 1.0.1, but all files remain unchanged.
v1.0.0
Initial release of Mac Mini AI — local AI server and fleet for Mac Mini: - Enables running LLMs, image generation, speech-to-text, and embeddings locally on Mac Mini (M4 and M4 Pro). - Supports easy stacking of multiple Mac Minis into a managed AI fleet, with automatic device discovery and request routing. - Provides simple setup, OpenAI-compatible API, and works without Docker or Kubernetes. - Includes dashboard, monitoring, and model recommendations per device. - Built-in guardrails: explicit user confirmation for model downloads and deletions; all requests processed locally.
元数据
Slug mac-mini-ai
版本 1.0.1
许可证 MIT-0
累计安装 2
当前安装数 2
历史版本数 2
常见问题

Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon 是什么?

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... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 111 次。

如何安装 Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install mac-mini-ai」即可一键安装,无需额外配置。

Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon 是免费的吗?

是的,Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon 支持哪些平台?

Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin)。

谁开发了 Mac Mini AI — Mac Mini Local LLM, Image Gen, STT on Apple Silicon?

由 Twin Geeks(@twinsgeeks)开发并维护,当前版本 v1.0.1。

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