Impactful scheduling for GPU clusters

An AI research institute redesigned how it allocates GPU computing resources to research projects by replacing a priority-based scheduler with a system using GPU time budgets and fair-share allocation. The change matters because the institute faced a common resource allocation problem where demand far exceeded supply, leading to inefficient behaviors like users parking unused workloads to reserve capacity and priority inflation where nearly all jobs claimed the highest priority level. Under the new system, leadership allocates a percentage of total GPU time to each project in advance based on strategic importance rather than handling allocation decisions case-by-case, making resource requests transparent and preventing users from gaming the system since any strategy to obtain GPU time now draws from a team's predetermined budget. The institute manages thousands of GPUs used for large-scale AI model training and other computationally intensive research across approximately 150 internal researchers.