Google released TorchTPU, its native PyTorch stack for Google TPUs, as open source on GitHub and shipped version 1.0 of llm-d, an open-source distributed inference stack that runs on Kubernetes. Both were announced at PyTorch Conference 2026, according to a Google Cloud blog post by Mark Lohmeyer, vice president and general manager of AI and computing infrastructure, and Bill Jia, vice president of engineering.

"With TorchTPU, TPUs join GPUs and CPUs as a native option for PyTorch," the post said. Google said TorchTPU was co-developed with the PyTorch team at Meta and lets teams run existing PyTorch workloads on TPUs with minimal code changes, debugging in eager mode and then moving to torch.compile for production performance.

Rows of server racks with glowing accelerator boards connected by streams of light to a central routing node in a data center

Meta said it is using TorchTPU on key production use cases. "We cannot be constrained by a single hardware architecture," said Jana van Greunen, Director of PyTorch Engineering at Meta, in the Google post. She said TorchTPU supports major LLM, ads and recommendation workloads at Meta and lets the company deploy PyTorch workloads to Google TPUs.

What Runs on TorchTPU

Google said it partnered with Hugging Face so that open-weight models on the Hugging Face Hub run natively on TorchTPU. In Transformers v5, deploying a model to TPUs takes passing device_map="tpu", the post said. TorchTPU also works with the vLLM and SGLang serving engines, the TorchTitan training framework, the VeRL reinforcement learning framework and Ray.