Anyscale, Google Cloud add native Ray support for PyTorch on TPUs

Anyscale and Google Cloud said on Oct. 20, 2026, that Ray now has native support for PyTorch workloads on Google TPUs, extending the PARK stack into TPU-based training and inference on Google Kubernetes Engine. The companies said the integration is built on Google’s TorchTPU project and KubeRay on GKE, and is designed to give PyTorch users a single-device-per-worker model on Cloud TPUs. In the announcement, Anyscale said platform and ML teams running on GKE can schedule, scale and operate distributed PyTorch training, vLLM inference and reinforcement learning workloads across GPUs and TPUs on a single Kubernetes control plane. The company said Ray workers on TPUs now map one worker to one TPU chip, using resources={"TPU": 1} and device isolation through TPU_VISIBLE_CHIPS , while switching PyTorch code from cuda to tpu…

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