Ready-made: all your AI work on your own hardware. Not a single byte leaves the machine — code, client data and NDA material stay with you. No subscriptions, can run fully offline.
What you get
- A local "AI stack": language models, a coding agent, image generation and video editing — all offline.
- Full privacy: sensitive data never goes to someone else's cloud.
- Independence from subscriptions and limits.
- (Optional) a single OpenAI-compatible endpoint any tool can connect to.
What you need
Tools from the base: Ollama (models), Aider or Cline (coding agent), LiteLLM (unified proxy, optional), ComfyUI + FLUX/SDXL (images), DaVinci Resolve or OpenCut (video), the Context7 MCP (docs). Software is $0; the real cost is hardware: a GPU with 8–24GB VRAM or Apple Silicon with 16–32GB+ RAM, plus 20–100GB disk for weights.
Step by step
- Language models — Ollama:
ollama run llama3.2(or qwen2.5 / mistral / deepseek),ollama pull qwen2.5-coder. OK when it answers offline. - Coding agent — Aider or Cline pointed at the local Ollama endpoint (
http://localhost:11434):aider --model ollama/qwen2.5-coder. OK when requests go to localhost, not the network. - Unified proxy (optional) — LiteLLM as an OpenAI-compatible gateway to local models (plus cost logs) for tools that only speak OpenAI. OK when such a tool works via
http://localhost:4000. - Images — ComfyUI + local weights (FLUX/SDXL), generated offline. OK when it renders with the network off.
- Video editing — DaVinci Resolve or OpenCut, render locally without uploading footage. OK when the export comes off your own disk.
- Docs for the AI — the Context7 MCP (on-demand) or pre-downloaded docs for full offline. OK when the agent cites current APIs.
Free vs fast (paid)
This path is free by software — you pay in hardware and setup time. Budget: 7B/quantized models, SDXL on 8GB. Fast: 30–70B models and FLUX on 16–24GB. If privacy is not critical for a task, the cloud is faster and smarter (see "Vibe-coding for free").
Common problems
Out of VRAM → smaller or quantized (Q4_K_M) model, SDXL over FLUX. Slow replies → normal locally; smaller model/context or stronger GPU. Tool needs OpenAI API → add the LiteLLM proxy. Lower quality on hard tasks → expected; split the task or offload that piece to the cloud. Need full offline → pre-download docs instead of Context7.
Time & money
Setup 30–90 min (plus weight downloads). Software $0; real cost is hardware, from "already have a suitable PC/Mac" to buying a GPU. The price of privacy is hardware and patience, and local models trail top cloud ones — but data never leaks.