No idea. My best guess is their background is in graphics and games rather than machine learning. When CUDA is all you've ever known, you try just a little harder to find a way to keep using it elsewhere.

People know; it just hasn't been reliable.

What's not reliable about it? On Linux hipcc is about as easy to use as gcc. On Windows it's a little janky because hipcc is a perl script and there's no perl interpreter I'll admit. I'm otherwise happy with it though. It'd be nice if they had a shell script installer like NVIDIA, so I could use an OS that isn't a 2 year old Ubuntu. I own 2 XTX cards but I'm actually switching back to NVIDIA on my main workstation for that reason alone. GPUs shouldn't be choosing winners in the OS world. The lack of a profiler is also a source of frustration. I think the smart thing to do is to develop on NVIDIA and then distribute to AMD. I hope things change though and I plan to continue doing everything I can do to support AMD since I badly want to see more balance in this space.

The compilation toolchain may be reliable but then you get kernel panics at runtime.

I've heard geohot is upset about that. I haven't tortured any of my AMD cards enough to run into that issue yet. Do you know how to make it happen?

Last time I used AMD GPUs for GPGPU all it took was running hashcat to make the desktop rendering unstable. I'm sure leaving it run overnight would've gotten me a system crash.

That's always happened with NVIDIA on Linux too, because Linux is an operating system that actually gives you the resources you ask for. Consider using a separate video card that's dedicated to your video needs. Otherwise you should use MacOS or Windows. It's 10x slower at building code. But I can fork bomb it while training a model and Netflix won't skip a frame. Yes I've actually done this.