Today at PyTorch Conference 2026, we announced a range of new products and integrations designed to help you run PyTorch effectively on Google Cloud or in your own infrastructure. Notably, TorchTPU, our native PyTorch stack for Google TPUs, is now available as open source on GitHub. With TorchTPU, TPUs join GPUs and CPUs as a native option for PyTorch. You can now train, serve, and deploy models on TPUs using the tools you already rely on, including vLLM, SGLang, llm-d, TorchTitan, Ray, TorchRec, Hugging Face, LiteRT. Ultimately, this provides our customers choice across a broad range of workload-optimized computing options including TPUs, GPUs, and CPUs, with a consistent, open software and operational model across the full portfolio.
This project provides a PyTorch backend for Google's Tensor Processing Units (TPUs), enabling users to run PyTorch models and operations on TPU devices. It includes custom ATen kernels for various PyTorch operations, a compilation cache for optimizing execution, and examples for distributed training and model inference.
See the GitHub project.