GoKawiilGoogle open-sources TorchTPU, a PyTorch backend for its TPU chips
Google has released TorchTPU, an open-source project that lets PyTorch models run directly on its Tensor Processing Units. The package includes custom ATen kernels, a compilation cache for optimizing XLA computations, support for distributed training, and sample implementations for models like Llama, Qwen, ResNet and UNet. It is available via pip as version 0.2.0 or can be built from source using Bazel.
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By giving PyTorch users a native path to TPUs rather than requiring conversion to other frameworks, Google could make its custom chips more attractive to the large base of developers who already default to PyTorch. This may position TPUs as a more direct competitor to Nvidia GPUs for training and inference workloads, though actual adoption will depend on performance parity and ecosystem support.
- TorchTPU provides a native PyTorch backend for running models on Google TPUs
- The release includes distributed training support and example models like Llama and ResNet
- It's installable via pip (version 0.2.0) or buildable from source with Bazel
Source: github.com, 2026-10-20
Published there as: “Google open-sources TorchTPU, a native PyTorch back end for TPUs”
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