If you found this post, you are early. RunAIHome is brand new, and this is the welcome note.

The premise

The local AI scene has matured fast. A consumer GPU with 12 to 24 GB of VRAM can now run image generation, video generation, and respectable language models entirely offline. The bottleneck is no longer hardware — it is knowing which combination of card, model, quantization, and tool actually works for what you want to do.

That is the gap this site exists to fill.

What we will cover

Four lanes, all aimed at the same audience — the person who buys a GPU partly to game and partly to run AI on their own terms:

1. GPU benchmarks for AI — repeatable Stable Diffusion, ComfyUI, Flux, and local LLM numbers across the cards people actually buy. No marketing slides, no synthetic-only scores.
2. Local LLM tutorials — Ollama, llama.cpp, LM Studio, quantization choices, context length tradeoffs. The unglamorous parts that determine whether a 70B model will run on your machine.
3. ComfyUI and image generation — node setups, LoRA training on limited VRAM, model selection for specific styles and tasks.
4. Self-hosted AI stack — Open WebUI, n8n automations, privacy-first plumbing for the home lab.

First wave in the queue

Hardware ships in a few days, and the first round of posts is being written now:

• RTX 5060 Ti for AI: real benchmarks across SD 1.5, SDXL, Flux, and local LLMs
• 5060 Ti vs 4070: which is actually worth it for local AI in 2026
• Setting up ComfyUI on Windows in under 10 minutes
• Ollama vs LM Studio vs llama.cpp: when each one makes sense

If a topic is missing from that list and you want it covered, the contact details on the About page will be live shortly. Save the RSS feed and check back in two weeks.