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You can run stable diffusion on a MBP and produce images in under a minute. It's training these models that takes the crazy GPU power - running them is quite reasonable.

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Still can't run GPT-3 or even GPT-J locally though, which is what the article is about. Learning takes a whole datacenter with actual terabytes loaded into VRAM, sure, but even running it requires you to have enough space on the card to actually load the model. Which is usually still in the 20G+ range.

Stable diffusion is about the only one of these useful groundbreaking models that can run on normal hardware to some extent, and even that's extremely limited with only like what, 256x256 being possible with a 6G card and 512x512 on a 10G card? So thanks for pointing out the one partial exception.

With stable diffusion Im making right now an image every 26 seconds at 512x512 with 50 sampling steps with https://github.com/JoePenna/Dreambooth-Stable-Diffusion

The training with a beefy GPU from vast.ai (RTX 3090 with 24vram) and Im generating the images with a GTX 1080 with 4vram, so no need for 6 or even 10 GVram from my testing

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