Google Open-Sources TorchTPU, Bringing Native PyTorch to Its TPU Chips

Google has open-sourced TorchTPU, a native PyTorch backend for its Tensor Processing Units (TPUs). Google announced it on October 20, 2026 at PyTorch Conference 2026, and the code is now on GitHub.

What it does

TorchTPU was co-developed with the PyTorch team at Meta. It lets developers run existing PyTorch workloads on TPUs with minimal code changes. According to the PyTorch announcement, porting a model should be as simple as changing model.to("cuda") to model.to("tpu"). The team notes that reaching peak TPU performance still takes more work, because a model’s shapes, sharding and ops may be tuned for GPUs.

It supports PyTorch’s eager mode for debugging, torch.compile for production speed, and PyTorch’s distributed tools, and it works with projects like vLLM, SGLang, TorchTitan, Ray and Hugging Face.

How well it works so far

The PyTorch team says it tested 4,917 popular open-source Hugging Face models on TPUs. 98% of them (4,821) ran without crashing, and 97% of a smaller set of 657 architecture representatives matched GPU results numerically.

TorchTPU is still in active development, but the team says it is already used in production for inference and training by some of the largest Google Cloud customers.

Why it matters

TPUs have historically been tied to Google’s JAX and TensorFlow ecosystems. With TorchTPU, Google says, TPUs join GPUs and CPUs as a native option for PyTorch, giving developers another choice of hardware without rewriting their code.

What’s next

Alongside TorchTPU itself, the team says it is open-sourcing companion repositories for vLLM-TorchTPU, SGLang-TorchTPU and TorchTitan, and its roadmap for the next 6 to 12 months focuses on production hardening, wider operator coverage, and low-bit FP8 and FP4 support for future TPU generations.