RAM. GPT-3 is over 600GB, ie just the max RAM of 8xA100s, because that's all the hardware can fit.
StableDiffusion plus a whole chain of imagenets can make any visual imagery imaginable in 2GB of RAM. Meanwhile 2GB of RAM barely runs a basic tiny text completion NN that can't do anything intelligent. Text requires a lot more parameters (and more memory/RAM) than images.
The Cerebras node's actual "RAM" (the 40GB of SRAM) is pretty modest too, but being an enormous chip with the networked storage pools is certainly a better situation than a bunch of A100s reaching out to every other A100.
Honestly, all the AI ASIC makers drastically underestimated the RAM requirements of future models. Graphcore's 4GB and Tenstorrent's 8GB per IC is kinda laughable, and it takes them longer to adjust than Nvidia. And Cerebras' original pitch was "fit the entire model into SRAM!"
StableDiffusion plus a whole chain of imagenets can make any visual imagery imaginable in 2GB of RAM. Meanwhile 2GB of RAM barely runs a basic tiny text completion NN that can't do anything intelligent. Text requires a lot more parameters (and more memory/RAM) than images.