In some cases one can replace rocm/openvino incompatible libraries with pure PyTorch, but the performance hit is often severe, and not many inference projects are using torch.compile or triton kernels to compensate.
Performant Pytorch compatibility can be achieved without CUDA compatibility.
> In some cases one can replace rocm/openvino incompatible libraries with pure PyTorch
This is the crux of my point: AMD ought to focus on fixing these Pytorch incompatibilities instead of splitting focus with chasing CUDA translations.
The point really is that while deep learning libraries are amazing, at the end of the day they are DSL and really pull towards one specific way of computing and parallelization. It turns out that way of parallelizing is good for deep learning, but not for all things you may want to accelerate. Sometimes (i.e. cases that aren't dominated by large linear algebra) building problem-specific kernels is a major win, and it's over-extrapolating to see ML frameworks do well with GPUs and think that's the only thing that's required. There are many ways to parallelize a code, ML libraries hardcode a very specific way, and it's good for what they are used for but not every problem that can arise.
Not that it isn't appealing... If the price is right.
Also, both AMD and Intel are both reportedly coming out with M Pro-like SoCs. AMD's "Strix Halo" is rumored for 2025. Less is known about Intel's Arrow Lake SKU at the moment.