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GKE dynamic slicing on TPUs

Running large-scale AI workloads on TPU and Google Kubernetes Engine (GKE) requires infrastructure that adapts as fast as your models change. Dynamic Slicing is generally available on GKE, which decouples logical TPU slice formation from physical nodepool provisioning.

Compared to creating a static nodepool for each TPU slice, dynamic slicing reduces AI workload startup times by up to 5x and workload recovery by 4.5x — maximizing TPU goodput, efficiency, and workload resilience at scale.

Beyond one workload, one nodepool

Previously, managing TPU capacity at GKE standard required separate, fixed TPU slice topology nodepools, increasing startup times and complexity while preventing multi-host nodepools from hosting multiple AI workloads. Changing workload needs meant manually deleting and recreating nodepools. Additionally, this tight coupling created large failure domains where a single hardware fault crashed an entire slice, slowing recovery while reprovisioning.

To overcome these challenges, we built GKE dynamic slicing, which decouples physical hardware...

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Cloud Pathways, a distributed ML runtime, is GA

Today, we are thrilled to announce the general availability (GA) of Cloud Pathways (Pathways on Cloud) on Google Kubernetes Engine (GKE) as a core runtime pillar of the Google Cloud AI Hypercomputer. Originally engineered by Google DeepMind to train and serve Google's frontier models — including Gemini — Pathways is

Beyond one workload, one nodepool

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Cloud Pathways, a distributed ML runtime, is GA