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mac-mini-aimac 迷你 ai

Agent Skill

mac-mini-ai 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,744

周安装

150

GitHub Stars

2

下载量

1,212
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mac-mini-ai(mac 迷你 ai)
来源仓库:https://github.com/twinsgeeks/mac-mini-ai
安装命令:
openclaw skills install mac-mini-ai
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install mac-mini-ai

简介

在 M4/M4 Pro 芯片 Mac Mini 上运行大模型与图像生成任务。

  • 适用于本地 AI 推理、语音转文本等研究检索需求。
  • 支持嵌入向量计算与媒体处理能力,适合边缘部署。mac-mini-ai 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 高负载任务可能导致设备发热,需确保良好散热条件。
  • 模型加载依赖本地存储空间和 RAM 容量限制。

SKILL.md

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

ConfigChipUnified MemoryPriceLLM Sweet Spot
Mac Mini M4 (16GB)M416GB$5993B-7B models (phi4-mini, llama3.2:3b)
Mac Mini M4 (24GB)M424GB$7997B-14B models (phi4, gemma3:12b)
Mac Mini M4 (32GB)M432GB$99914B-22B models (qwen3:14b, codestral)
Mac Mini M4 Pro (48GB)M4 Pro48GB$1,39922B-32B models (qwen3:32b)
Mac Mini M4 Pro (64GB)M4 Pro64GB$1,79932B-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 "file=@meeting.wav" -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

RAMBest modelsWhy
16GBphi4-mini (3.8B), gemma3:4b, nomic-embed-textSmall but capable, leaves room for OS
24GBphi4 (14B), gemma3:12b, codestralSweet spot for single-model use
32GBqwen3:14b, deepseek-r1:14b, codestral + phi4-miniTwo models simultaneously
48GBqwen3:32b, deepseek-r1:32bLarger models, great quality
64GBllama3.3:70b (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

ToolConnection
Open WebUIOllama URL: http://mac-mini-ip:11435
Aideraider --openai-api-base http://mac-mini-ip:11435/v1
Continue.devBase URL: http://mac-mini-ip:11435/v1
LangChainChatOpenAI(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/.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

96.81%
按下载量换算1,173

安全审计

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该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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