Hugging Face ❤️ TorchTPU: TPUs, Natively

Hugging Face has introduced TorchTPU, a native PyTorch backend that treats Google TPUs as a standard device, allowing Transformers, Diffusers, and TPU-optimized kernels to run without requiring PyTorch/XLA, JAX, or Optimum TPU. Built for Google’s Tensor Processing Units, which are specialized accelerators for neural-network matrix computation and can scale from individual chips to large pods, TorchTPU supports ordinary eager PyTorch execution, optional torch.compile optimization, distributed training, and tensor parallelism for models too large for one chip. Users can load Hugging Face models directly with device_map="tpu" or move Diffusers pipelines using pipe.to("tpu"), while compiled execution can reuse graphs for repeated workloads of the same size. The release also integrates Pallas-based kernels from the Hugging Face Kernels Hub and Tokamax, with reported decoding improvements at large batch sizes for selected mixture-of-experts models. Hugging Face is expanding TPU test coverage in Transformers, reporting a 96.5% pass rate among applicable tests, 34 identified TorchTPU issues, and 19 fixes, while inviting users to install the package, experiment with Hub models, and report problems.

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