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Hugging Face ❤️ TorchTPU: TPUs, Natively

AnnouncementProductOct 20, 2026

Hugging Face announced TorchTPU, a native PyTorch backend that lets Transformers, Diffusers, and Kernels run directly on Google's TPUs. Users can load Hub models with device_map="tpu" without Optimum TPU or torch_xla. TPUs, which Google uses to train and serve Gemini, are available on Google Cloud. Hugging Face reports 96.5% of Transformers tests pass on TPU, with 34 issues filed and 19 already fixed.

Why it matters

Hugging Face has released TorchTPU, a native PyTorch backend that treats Google's Tensor Processing Units as a standard PyTorch device. Previously, PyTorch users who wanted to run models on TPUs had to go through PyTorch/XLA, which the company describes as awkward: lazy tensors, unpredictable execution, and painful debugging, or else switch to JAX. With TorchTPU, users of Hugging Face's Transformers and Diffusers libraries can load a model from the Hub and place it directly on a TPU, with no adapter layer required. Hugging Face says most of the Transformers test suite now passes on TPU hardware, and that failures are being tracked as issues on the TorchTPU repository, with many already resolved. The move matters because TPUs, the chips Google uses to train and serve Gemini, have historically been tied to Google's own software stack, and this opens them to the broader PyTorch ecosystem.

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Evidence

1source· awaiting independent confirmation

No score is assigned. Sources and their independence are shown in the citation chain below.

Citation chain · 1 source

01high
huggingface.coOfficialhttps://huggingface.co/blog/hf-torchtpuThis is Hugging Face's own announcement, explaining that TorchTPU makes TPU a native PyTorch device and that Optimum TPU is no longer needed to run its models on Google's accelerators.
PRIMARY
GoogleCompanyGeminiModelHugging FaceCompany

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Canonical: https://huggingface.co/blog/hf-torchtpu