We took a stock Asus TUF gaming laptop with an RTX 3070 and 8 GB of video memory, and benched eight local models overnight across ten tasks. The result that surprised us: a 30 billion parameter model with two thirds of itself off the card beat a 14 billion parameter model that fit better, by nearly four times. File size does not predict speed. All 103 measured rows are free below, failures included.
Someone asked this morning which models to back up now that NVIDIA is buying Hugging Face. We told people to keep local copies a week ago, and four days ago we wrote about our own disk filling up and breaking our publishing. Both of those are true at once, so here is the version that reconciles them.
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.
Twenty generators, eight prompts with invented specifics, one shot each, the prompt on every board. New this round: five Krea 2 builds, Qwen Image 2512, a real cloud column priced in Buzz, and a text ladder scored over four extra seeds. Every board at full size, every prompt in all three styles.
A published episode scored 86 failures against its own script. Here is every class of mistake we found making local AI video with LTX 2.5, SageAttention, Krea 2 and ComfyUI, what actually caught each one, and the checks we now run before anything ships.
Two unrelated things happened on the same Thursday. The place almost every open model lives got acquired by the company that makes the chips, and the three biggest chatbots went down together because they share a cloud. Here is what we think it means for anyone running AI on their own machine, including the part where our own pipeline broke this morning.
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.
Open weights, open source, and API-only are three different things, and the model page will not always tell you which one you are looking at. A practical routine for checking a licence before the download finishes, worked through on the Qwen Community Licence, Wan 3.0, and Apache 2.0.
Twelve generators, six prompts, one shot each, no cherry-picking, with the numbers nobody publishes: seconds per image, VRAM required, and the exact cloud price. Six of them run free on your own card.
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.