GKE Pod snapshots
When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need to execute code instantly. If each component is starting “cold” with a full data-load process, all this provisioning takes time, often forcing organizations to overprovision their infrastructure just to meet scaling requirements.
To solve this, we introduced Google Kubernetes Engine (GKE) Pod snapshots, a new feature that lets you save the running state of your workload, including CPU and GPU memory, and restore it on demand.
GKE Pod snapshots reduce AI inference start-up by as much as 89%, loading 70B parameter models in just 37 seconds and 8B parameters models in just 15 seconds. This speed allows your infrastructure to scale as fast as your demand, significantly reducing the need for overprovisioning.
The high cost of cold starts —...
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