What is Pathways? Single-controller training for large models

Google Cloud has announced the general availability of Pathways, Google’s distributed system for training large JAX models such as Gemini on TPUs. Pathways addresses limitations of the common SPMD model, in which identical programs run in lockstep across accelerators and can become inefficient for pipeline parallelism, reinforcement-learning workflows, cross-group communication, resource sharing, and hardware failures. Using a single Python client, developers describe an end-to-end computation as a graph of programs, devices, and tensor dependencies, while the runtime maps virtual devices to physical hardware, transfers and reshards data, schedules operations asynchronously, and maintains a consistent execution order. This design supports MPMD workloads in which different stages run different programs, improves accelerator utilization through overlapping setup and computation, enables elastic recovery by remapping work after host failures, and allows multiple users to share a persistent TPU cluster. Pathways is positioned as complementary to Ray: Ray orchestrates Python workloads and cluster resources, while Pathways changes the underlying distributed execution model to coordinate model computation across many accelerator groups.

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