GPU and TPU utilization with multi-cluster GKE Inference Gateway
Demand for AI infrastructure is at an all-time high. Global accelerator shortages mean engineering teams can rarely get all the compute they need from just one data center — capacity comes a cluster here, a cluster there, often an ocean apart. At the same time, workloads are getting hungrier: Today’s long-running agentic workloads often have context windows of 100k to 800k+ tokens, which consume accelerator memory faster than any previous generation of AI traffic.
In this environment, the goal is to maximize "intelligence per dollar." Fragmented, poorly balanced infrastructure is rarely up to the task though, allowing expensive accelerators to sit idle, while requests queue up somewhere else.
To close that gap, we built a layered routing architecture that makes globally scattered capacity behave like a single pool behind a single entry point. At the edge, the multi-cluster GKE Inference Gatewayfocuses on global, multi-region traffic distribution and high availability....
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