Somebody on r/LocalLLaMA published their working on whether to buy or rent an eight GPU H200 server. We ran the same arithmetic for the card most people reading this would actually put in a desktop, and the answer has moved a long way in the last year. It is not the answer we expected, and we own the card.
A Mozilla analysis put the open to closed capability gap at about 4.4 months, and it went round the local AI world as good news. It is good news. It is also being read as something it does not say, because the open model doing the catching up is 2.78 trillion parameters and will never see the inside of your computer.
Everyone plans their VRAM. Almost nobody plans their storage, and it fails in ways that do not look like storage. A note on the hidden running cost of local AI, including the 591 GB folder we did not know we had and the broken login it caused.
Both of those happened on the same day, in the same corner of the internet, running comparable work. Prices at the top are climbing, the used enterprise shortcut is failing in public, and the floor keeps quietly dropping. A note on the strangest hardware ladder local AI has had yet.
VRAM first, everything else second: what GPU you actually need for local AI in 2026, the memory-crisis market, the value picks, AI appliances like DGX Spark, four real builds by budget, and when renting beats buying.
The images, the video, the 3D avatar and the daily uploads on this channel run on one prebuilt gaming PC. Here’s the exact spec, what actually matters for local AI, and what we’d change.
Pick your GPU and RAM, get honest answers: which AI chat models, image generators, voice cloning, talking avatars, video generation, and full AI companions your machine can actually run, from people who run all of it daily.