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So the question I have now is which one is faster/better suited for my puropses. M1 got [hyped](https://machinelearning.apple.com/updates/ml-compute-training-on-mac) a lot so I thought the M1 would savage my desktop (and acutally the hype biased my purchase decision), but well its only slightly better (like 1.2-1.5x faster in my cifar10 benchmark) and I wonder if its worth the effective 1-2 GB of RAM left on MacOS vs the \~14 GB on my Linux machine. Further there is Colab and I can't really tell which one will win the race, since Colab limits resources by demand but also allows distributed fit on cloud TPUs, which would introduce some extra coding efforts. Then again I have to say: so does ML on Apple Silicon, which comes with [a handful of limitations](https://github.com/apple/tensorflow_macos#additional-information), a [peculiar MiniConda setup](https://github.com/apple/tensorflow_macos/issues/153), a [lot of issues](https://github.com/apple/tensorflow_macos/issues) (also severe ones, like training errors etc., problems which I would not even recognize) which are actually not really being worked on.