That's the rub: deep learning is restricted to CUDA/cuDNN/NVIDIA GPUs, which exclude a large amount of desktops with AMD graphics (e.g. all modern Macs).
Current cloud GPU prices have dropped enough such that this approach may be pretty effective with spot/preemptible instances.
Developing competitive CUDA equivalent is much harder task than what you assume. There needs to be lots of tooling and need for optimizations in software to make these low level libraries high performance. The amount of testing and benchmarking to make the right choices may take many people working full time. Implementing the functionality is not enough.
AMD has had apis for a long time and it has an has an open source deep learning stack but it's not good enough. AMD also has CUDA to HIP converter but the results are not competitive and miss features.
Both AMD and Intel are probably getting there eventually and Nvidia is cashing the monopoly phase before the prices stop.
i’d be thrilled to have the option of doing deep learning on an AMD card even if it ran at 1/3 the speed on a comparable (but probably somewhat more expensive) NVIDIA card. it would open up a lot of options even if it were still less economical in throughput/dollar.
if nothing else, how many machine learning researchers would like to prototype things on their MacBook Pros?
They are even ok for some kinds of training (eg, if you are doing transfer learning with a fixed embedding/feature representation and have a pretty small parameter space to learn).
But it's so easy to fire up a cloud instance at Paperspace or somewhere and push some code across.
And whose fault is that?