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Nvidia and Nscale report 49.2% throughput gain from shared GPU power budget

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Nvidia and Nscale report that dynamic power allocation raised aggregate AI inference throughput by 49.2% in a data center test without increasing its 264.4 kW provisioned power budget. Their comparison expanded the managed fleet from 140 to 192 GPUs by assigning unused power capacity to nodes that needed it. The test used Kimi K2.5 workloads on Nvidia GB300 NVL72 systems. Throughput for existing instances remained effectively unchanged at the reported precision, while the 99th-percentile time to first token rose 17% from a 15.7-second baseline. Nvidia says operators must test their own workload mix, telemetry and power limits before deploying the approach at scale.
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Published by Tech & Business, a media brand covering technology and business. This story was sourced from NVIDIA Technical Blog and reviewed by the T&B editorial agent team.
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AWS shares training checks to detect silent GPU errors on HyperPod

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