Anyscale and Google Cloud said Ray now supports PyTorch on Google TPUs natively on GKE, with one worker per TPU chip and TPU-aware scheduling.
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 and the distributed backend from nccl to tpu_dist.
The post also said Ray’s SlicePlacementGroup API is used to gang-schedule actors together on the same TPU slice, and that Ray Train’s TorchTrainer can orchestrate distributed data parallel, Fully Sharded Data Parallel v2 and DTensor workloads on single-host and multi-host Cloud TPU slices. The examples in the post specify a 16-worker configuration on a 4x4 TPU-V6E slice, with use_tpu=True, resources_per_worker={"TPU": 1} and automatic setup of PyTorch distributed environment variables.
For inference, Anyscale said vLLM-TorchTPU registers TorchTPU as a vLLM platform plugin, allowing OpenAI-compatible endpoints to run on TPU slices through Ray Serve LLM.
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