That wasn't my point.

My point was that many people want to buy or already own AMD GPUs with 8gigs or more and they're basically excluded from playing with the hip ML stuff unless they jump through insane hoops because all the hip ML stuff is done on Nvidia GPUs.

It wasn't your point, but it was mine. In terms of raw power this should be in range of AMD hardware. Needs porting work, yes, but I have a feeling the hoops ought to be less insane than all that. It's not "recompile with a different library", quite, but if you take a look at the sort of portability work going on in (for instance) llama.cpp you can see reasons for optimism.

Is that because AMD GPUs are technically inferior for ML, or because of tooling is only written for NVIDIA?

A bit of both, NV has put a ton of work in their software libraries which makes doing some stuff on NV a viable option, you'd have to do a lot of work to get to an equivalent level on AMD.

But if all you need is PyTorch support then AMD may well be a viable option.

Maybe AMD should start supporting their older cards in ROCm...

Maybe they should support their modern consumer cards in ROCm. Maybe their ROCm documentation should not suck balls.

I'd say there is a reason AMD is a laughing stock in ML, but it's incorrect. There's not a reason, there are tons of reasons to never touch it.