> the statement you're making about AMD GPUs only "being fine in limited circumstances" is absolutely false

Sorry, but there are limitations to the circumstances in which AMD cards are "fine". There are many real-world cases where Nvidia/CUDA is currently required for something to work. The comment you replied to was:

> Limited use in neural network applications at present due to many application's CUDA requirements (though the same could be said of AMD)

It was not specificaly about "code that is pure PyTorch", nor self-developed systems, but neural network applications in general.

It's fair of you to say that CUDA requirements can be met with HIP and ROCm if the developer supports it, though there are numerous issues and flaws in ROCm itself. But there are still issues and limitations in some circumstances, where they don't, as you've just described yourself! You can say that's due to the "laziness" of the developer, but it doesn't change the fact that it's broken. At the least it requires extra development time to fix, if you have the skills. I know a lot of people would appreciate it if you would convert the bitsandbytes library! Just because it could work, doesn't mean it does work.

The idea that there's just no downside to AMD cards for ML, because of the existence of ROCm, is true only in limited circumstances. "Limited" does not mean "very few", it means that ROCm is not a perfect drop-in replacement for CUDA in all circumstances; there are issues and limitations. The fact that Dreambooth doesn't run on AMD proves the point.