Hugging Face ❤️ TorchTPU: TPUs, Natively

Hugging Face wait, what’s a TPU? 🤔 The old way Enter TorchTPU 🚀 Your favourite libraries, now on TPU Transformers Images?

Videos? Diffusers paints them on TPUs 🎨 Kernels We test it so you don’t have to 🧪 Get started If you ever tried to run PyTorch on a TPU, you probably ported your code to torch_xla, or you ended up having to learn JAX. With TorchTPU, a native PyTorch backend, TPU is now just another device. It opens up native support of TPU in the Hugging Face ecosystem. Transformers, Diffusers and Kernels run natively on TPU. Pick a model from the Hub, send it to “tpu”, and you’re done. TPU stands for Tensor Processing Unit. TPUs are Google’s own AI accelerators. They are chips built for one job: the matrix math that neural networks spend most of their time on. Google has been designing them for about ten years, and uses them to train and serve its own models, including Gemini. TPUs are available on Google Cloud, from a single chip up to pods of thousands of chips. They shine when you need a lot of compute, for training or for serving at scale. The catch has always been software. The TPU stack grew up around TensorFlow and JAX, and PyTorch users had to work harder to get in. Until now, running PyTorch on TPUs meant going through PyTorch/XLA (torch_xla).