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Zesty says its Automated Fractional GPUs service is available today, automatically adjusting GPU memory, sharing modes and workload placement as demand changes in Kubernetes AI deployments.

The system works alongside NVIDIA’s Run:ai GPU Fractioning Operator and Kubernetes scheduler. Zesty says it uses observed workload behaviour and teams’ performance policies to decide how capacity is shared, without requiring application or model changes. For operators trying to fit more AI work onto expensive GPUs, the promise is less stranded capacity and fewer reasons to buy more hardware. Whether that translates into savings without hurting performance is the useful test, not a detail to leave to the launch copy.

Discuss: Would you let an automated system reshuffle GPU resources in production to reduce costs, or should operators approve every change?

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