Dynamic GPU Capacity Controller Reallocates Idle Production GPUs to Research During Off-Peak Hours
By
Runway Team
Summary
The article describes how the authors built a capacity controller that dynamically reallocates GPUs between production inference workloads and research tasks based on daily demand cycles. Using queueing theory, they optimized GPU allocation so that production tracks demand without over-provisioning, freeing up more GPUs for research during off-peak overnight hours while reducing queue wait times throughout the day. The system builds on their previous work using Kueue to lend idle GPUs across research initiatives, now extending the concept to reclaim production GPUs for research during low-demand periods and return them before the morning peak.
Source
Twitter / XDynamic GPU Capacity Controller Reallocates Idle Production GPUs to Research During Off-Peak Hoursrunwayml.comKey quotes
· 3 pulledWe built a capacity controller that reallocates GPUs between production and research so production tracks demand without over-provisioning.
Using queueing theory we optimized allocations, leading to more GPUs for research overnight and shorter queue waits all day.
Here we focus on reclaiming production GPUs for research during off-peak hours, then returning them before the morning peak.
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