For LLM inference, basically everything works w/ ROCm on RDNA3 now (well, Flash Attention is via Triton and doesn't have support for SWA and some other stuff; also I mostly test on Linux, although I did check that the new WSL2 support works). I've tested some older APUs w/ basic benchmarking as well. Notes here for those interested: https://llm-tracker.info/howto/AMD-GPUs
Sadly, my feeling is that the big Strix Halo SKUs (which have no scheduled release dates) aren't going to be competitively priced (they're likely to be at a big FLOPS/real-world performance disadvantage, and there's still the PITA factor), but there is something appealing about about the do-it-all aspect of it.
The DGX documentation and downloads aren't public afaik.
Edit: Nevermind, some information about DGX is public and they really are on 22.04, but oh well, the deep learning stack is guaranteed to run.
It gets you closer for sure. But while ~250GB/s is a whole lot better than SO-DIMMs at ~100GB/s, the new mid-tier GPUs are probably more like 640-900GB/s.
Edit: The problem with AMD is less the hardware offerings, but more that their compute software stack historically tends to handwave or be very slow with consumer GPU support — even more so with their APUs. Maybe the advent of MI300A will change the equation, maybe not.