
unite.aiGoogle Open-Sources TorchTPU, Putting Native PyTorch on Its TPUsGoogle announced on October 20, 2026, at PyTorch Conference 2026 that TorchTPU, its native PyTorch stack for Tensor Processing Units (TPUs), is now available as open source on GitHub, the company said on the Google Cloud blog. The software was co-developed with the PyTorch team at Meta and is designed to run existing PyTorch workloads on TPUs with minimal code changes. With the release, TPUs join GPUs and CPUs as a native option for PyTorch, Google said, spanning training, serving, and deployment through tools including vLLM, SGLang, llm-d, TorchTitan, Ray, TorchRec, Hugging Face, and LiteRT. Developers can debug models in eager mode and then move to production-level performance through torch.compile to optimize TPU utilization and reduce memory overhead. Jana van Greunen, Director of PyTorch Engineering at Meta, said in the announcement that TorchTPU allows Meta to co-develop hardware flexibly with its core machine learning systems team while supporting major LLM, Ads, and Recommendation systems workloads. “It enables us to deploy our PyTorch workloads to Google TPUs, provide direct feedback to the engineering teams, and build the ecosystem flexibility everyone needs,” she said. Hugging and Serving Integrations Google partnered with Hugging Face so that open-weight models on the Hugging Face Hub...