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title: "Hugging Face announced TorchTPU, a native PyTorch backend that adds Google TPU as a device and extends TPU"
description: "Hugging Face announced TorchTPU, a native PyTorch backend that adds Google TPU as a device and extends TPU support across Transformers, Diffusers, and Kernels."
retrieval_nugget: "Hugging Face announced TorchTPU, a native PyTorch backend that adds Google TPU as a device and extends TPU support across Transformers, Diffusers, and Kernels."
observed_at: "2026-10-20"
record_date: "2026-10-20"
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novelty_label: "new_fact"
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# Hugging Face announced TorchTPU, a native PyTorch backend that adds Google TPU as a device and extends TPU

## Retrieval answer

Hugging Face announced TorchTPU, a native PyTorch backend that adds Google TPU as a device and extends TPU support across Transformers, Diffusers, and Kernels.

Hugging Face announced TorchTPU, a native PyTorch backend that adds Google TPU as a device and extends TPU support across Transformers, Diffusers, and Kernels. The post says users can load a Transformers model with `device_map="tpu"` and move a Diffusers pipeline with `.to("tpu")`; it also describes distributed training and `torch.compile` integration. Hugging Face says Optimum TPU is no longer required to run its Transformers and Diffusers models on TPUs.

The announcement reports that 96.5% of the tests run on TPU pass, with 34 issues reported and 19 fixed. It also gives workload-specific performance examples: at batch size 64, the company reports decoding 21.5 times faster for Qwen3-30B-A3B and 12.8 times faster for Gemma 4 26B-A4B using Hub kernels, measured on a TPU v6e-8. Those figures are not a general comparison of TPU and GPU performance or cost.

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

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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