Earlier quoted context omitted.
> most AI work loads are memory bound. Is that the reason why the number of neural cores stays the same, do you think?
The neural engine in Apple chips is really for small models for doing things like Face ID, local inference for Photos.app, etc. It's there to run those small models with very little power consumption. It's not that great for huge models. The GPU is better for those, and they have been adding cores and performance there. But yes, most large models are pretty heavily memory bound.
Is that true for discrete GPUs like Nvidia? Is it the same for unified memory like the M-series?