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If you want to use an Nvidia GPU you can buy them and put them in whatever you want.

If you want to use a TPU you have to use Google cloud.

Why would someone want a TPU?

Not just that but, why would someone (not working at Google) want a TPU so much to put up with all the hurdles, including learning a completely new and non-portable programming stack?

Most people don’t care what hardware it is running on as long as it can do fast matrix multiplications. Right now CUDA is the lowest friction way to do that.

XLA is the compatibility layer. You train using Torch/Jax/whatever you want, and XLA ensures you can use TPU. There aren't many hurdles, most people train in the cloud anyway.

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