The specific version of Nvidia CUDA or AMD ROCm that the GPU supports is often very important; some software needs to be compiled from a specific branch or with

The specific version of Nvidia CUDA or AMD ROCm that the GPU supports is often very important; some software needs to be compiled from a specific branch or with specific settings to support a given version. Case in point: the official PyTorch website offers four distinct builds for various platforms, two of those being different versions of CUDA. I think it'll still be useful for plenty of people to choose which GPU runs their code, even if it's compatible with any GPU offered by the service. You might want to choose an older, cheaper GPU for basic parallel computation, but as supply catches up with demand for a newer and more energy-efficient model you'll then want to switch to that. There are only so many GeForce 4090s to go round :)