If they can make a 288 GB $4.4-6.8k prosumer, home-computer-friendly graphics card, I will be extremely happy. Might be a pipe dream (today at least, lol, and standard in like...what, 5 years?), but if they can pull that off, then I think things would really change a lot.
I don't care if it's slow, bottom-of-the-barrel GDDR6, or whatever, just being able to enter the high-end model finetuning & training regime for ML models on a budget _without_ dilly-dallying with multiple graphics cards (a monstrous pain-in-the-neck from a software, engineering, & experimentation perspective)_ would enable so much large-scale development work to happen.
The compute is extremely important, and in most day-to-day usecases, the memory bandwidth even moreso, but boy oh boy would I love to enter the world offered by a large unified card architecture.
(Basically, in my experience, parallelizing a model across multiple GPUs is like compiling from code to a binary -- technically you can 'edit' it, but it's like directly hex editing strings in a binary blob, extremely limited. Hence why I try to stick with models that take only a few seconds (minutes at most) to train on highly-representative tasks, distill first principles, and then expand and exploit that to other modalities from there).