You are mixing up several levels of abstraction. It probably helps to realize that the GPU accelerated ML stuff was originally built on top of dedicated graphi

You are mixing up several levels of abstraction. It probably helps to realize that the GPU accelerated ML stuff was originally built on top of dedicated graphics libraries/APIs but diverged as it has different needs. You have here three somewhat distinct groups of things, but the boundaries are a bit fuzzy. Graphics interfaces: Vulcan, Metal ( and older OpenGL, D3D) Non-graphics GPU Compute interfaces: CUDA/(and RocM etc.) Computational Frameworks: Pytorch/Tensorflow/etc. The graphics interfaces are somewhat distinct from the ML/compute stuff, but as they use the same kind of underlying hardware to do work it's not that simple. The frameworks are trying to abstract computations from the underlying hardware, e.g. you can run your tensorflow/pytorch code against CPU only, etc. In practice though for "serious" work NVIDIA gpus are the likely target. The GPU interfaces are trying to do the same thing, roughly, but NVIDIA is far ahead in terms of hardware and dev support, so mostly that is what is deployed.