Google, working with Meta's PyTorch team, has released TorchTPU, an open-source native PyTorch backend for Google's Cloud TPUs. It runs eagerly by default, compiles through torch.compile and XLA, and is positioned as the replacement for PyTorch/XLA in new TPU workloads.

The release is part of a wider push to make it easier to run AI models on hardware other than Nvidia's. It landed one day after Amazon announced a similar approach for its Trainium chips, and the same day DeepSeek open-sourced DeepInfer, the inference engine behind its V4.1 Flash model, with Huawei Ascend support built in.

For developers, the practical effect is that PyTorch code can move to TPUs with less porting work, which matters as the labs and cloud providers compete on the cost of running models, not just on training them.