Skill: twinsgeeks/homelab-ai (version 1.0.2)

--- name: homelab-ai description: Home lab AI — turn your spare machines into a local AI home lab cluster. LLM inference, image generation, speech-to-text, and embeddings across macOS, Linux, and Windows devices. Zero-config mDNS discovery, real-time dashboard, 7-signal scoring. No cloud, no Docker, no Kubernetes. The home lab AI setup that just works. 家庭实验室AI本地推理集群。Laboratorio IA para inferencia local en casa. version: 1.0.2 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"house","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # Home Lab AI — Your Spare Machines Are a Cluster You have machines sitting around your home lab. A mini PC in the closet. A workstation on the desk. Maybe a desktop doing light work. Together, your home lab has more compute than most cloud instances — you just need software that treats them as one home lab system. Works on macOS, Linux, and Windows. Ollama Herd turns your home lab into a local AI cluster. One home lab endpoint, zero config, four model types. ## What your home lab gets ``` Device 1 (32GB) ─┐ Device 2 (64GB) ├──→ Home Lab Router (:11435) ←── Your apps / agents Device 3 (256GB) ─┘ ``` - **Home lab LLM inference** — Llama, Qwen, DeepSeek, Phi, Mistral, Gemma - **Home lab image generation** — Stable Diffusion 3, Flux, z-image-turbo - **Home lab speech-to-text** — Qwen3-ASR transcription - **Home lab embeddings** — nomic-embed-text, mxbai-embed for RAG All routed to the best available home lab device automatically. ## Home Lab Setup (5 minutes) ### On every home lab machine: ```bash pip install ollama-herd # Home lab AI router ``` ### Pick one home lab machine as the router: ```bash herd # starts the home lab router ``` ### On every other home lab machine: ```bash herd-node # joins the home lab fleet automatically ``` That's it. Home lab devices discover each other automatically on your local network. No IP addresses, no config files, no Docker, no Kubernetes. ### Optional: add home lab image generation ```bash uv tool install mflux # Flux models (fastest for home labs) uv tool install diffusionkit # Stable Diffusion 3/3.5 ``` ## Use Your Home Lab ### Home lab LLM chat ```python from openai import OpenAI # Home lab inference client homelab_client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") homelab_response = homelab_client.chat.completions.create( model="llama3.3:70b", messages=[{"role": "user", "content": "How do I set up a home lab NAS?"}], stream=True, ) for chunk in homelab_response: print(chunk.choices[0].delta.content or "", end="") ``` ### Home lab image generation ```bash curl -o homelab_output.png http://localhost:11435/api/generate-image \ -H "Content-Type: application/json" \ -d '{"model": "z-image-turbo", "prompt": "a cozy home lab with servers and RGB lighting", "width": 1024, "height": 1024}' ``` ### Home lab transcription ```bash curl http://localhost:11435/api/transcribe -F "file=@homelab_standup.wav" -F "model=qwen3-asr" ``` ### Home lab knowledge base ```bash curl http://localhost:11435/api/embed \ -d '{"model": "nomic-embed-text", "input": "home lab networking and AI inference best practices"}' ``` ## How the Home Lab Routes Requests The home lab router scores each device on 7 signals and picks the best one: | Home Lab Signal | What it measures | |--------|-----------------| | Thermal state | Is the home lab model already loaded (hot) or needs cold-loading? | | Memory fit | Does the home lab device have enough RAM for this model? | | Queue depth | Is the home lab device already busy with other requests? | | Wait time | How long has the home lab request been waiting? | | Role affinity | Big models prefer big home lab machines, small models prefer small ones | | Availability trend | Is this home lab device reliably available at this time of day? | | Context fit | Does the loaded context window fit the home lab request? | You don't manage any of this. The home lab router handles it. ## The Home Lab Dashboard Open `http://localhost:11435/dashboard` in your browser — your home lab command center: - **Home Lab Fleet Overview** — see every device, loaded models, queue depths, health - **Trends** — home lab requests per hour, latency, token throughput over 24h-7d - **Health** — 15 automated home lab checks with recommendations - **Recommendations** — optimal home lab model mix per device based on your hardware ## Recommended Home Lab Models by Device > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. | Home Lab Device | RAM | Start with | |--------|-----|-----------| | MacBook Air (8GB) | 8GB | `phi4-mini`, `gemma3:1b` | | Mac Mini (16GB) | 16GB | `phi4`, `gemma3:4b`, `nomic-embed-text` | | Mac Mini (32GB) | 32GB | `qwen3:14b`, `deepseek-r1:14b` | | MacBook Pro (64GB) | 64GB | `qwen3:32b`, `codestral`, `z-image-turbo` | | Mac Studio (128GB) | 128GB | `llama3.3:70b`, `qwen3:72b` | | Mac Studio (256GB) | 256GB | `gpt-oss:120b`, `sd3.5-large` | The home lab router's model recommender suggests the optimal mix: `GET /dashboard/api/recommendations`. ## Works with Every Home Lab Tool The home lab fleet exposes an OpenAI-compatible API. Any tool that works with OpenAI works with your home lab: | Tool | Home Lab Connection | |------|---------------| | **Open WebUI** | Set Ollama URL to `http://homelab-router:11435` | | **Aider** | `aider --openai-api-base http://homelab-router:11435/v1` | | **Continue.dev** | Base URL: `http://homelab-router:11435/v1` | | **LangChain** | `ChatOpenAI(base_url="http://homelab-router:11435/v1")` | | **CrewAI** | Set `OPENAI_API_BASE=http://homelab-router:11435/v1` | | **Any OpenAI SDK** | Base URL: `http://homelab-router:11435/v1`, API key: any string | ## Full documentation - [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) — all 4 home lab model types - [Image Generation Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/image-generation.md) — 3 home lab image backends - [Configuration Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/configuration-reference.md) — 44+ env vars - [Troubleshooting](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/troubleshooting.md) — common home lab issues ## Contribute Ollama Herd is open source (MIT) and built by home lab enthusiasts for home lab enthusiasts: - [Star on GitHub](https://github.com/geeks-accelerator/ollama-herd) — help other home lab builders find us - [Open an issue](https://github.com/geeks-accelerator/ollama-herd/issues) — share your home lab setup, report bugs - **PRs welcome** — from humans and AI agents. `CLAUDE.md` gives full context. - Built by twin brothers in Alaska who run their own home lab fleet. ## Home Lab Guardrails - **No automatic downloads** — home lab model pulls require explicit user confirmation. Some models are 70GB+. - **Home lab model deletion requires explicit user confirmation.** - **All home lab requests stay local** — no data leaves your home network. - Never delete or modify files in `~/.fleet-manager/` (home lab routing data and logs). - No cloud dependencies — your home lab works offline after initial model downloads.


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