Impactful scheduling for GPU clusters

Hugging Face overcommitting Tragedy of the commons Budgets not schedules Fair-share The scheduling contract Simulations Results Challenges The future Building a cluster scheduler to prioritize high-impact research while maintaining full occupancy On the AI Infrastructure team at Ai2, we’re responsible for providing the institute’s GPU compute capacity, specifically targeting large, distributed training workloads. We think about this task as a pyramid of four metrics that build on each other.

The foundation is availability: how often the hardware is healthy and ready for work. Above this is occupancy: the fraction of available time assigned to a specific workload. Next is impact: how often the most valuable workloads are chosen to receive resources. The capstone of the pyramid is utilization: the fraction of GPU capacity used over the lifetime of a workload. This post is about improving the impact of our scheduling decisions. We recently replaced a priority-based scheduler with a system including GPU time budgets, hierarchical fair-share allocation, and a time-slicing contract. As a result, we shifted the debate about how much GPU time each research project deserves from a case-by-case operational task to a transparent administrative budgeting process. At Ai2, we manage thousands of NVIDIA H100, B200, and B300 GPUs arranged in clusters ranging in size from 88 to 1024 GPUs. These clusters are built for large-scale distributed training of AI models, and they serve a group of about 150 internal researchers whose work covers a diverse set of AI domains, including the full model flow of LLM and VLM training, robotics reinforcement learning (RL) simulation, and post-training for scientific agentic use cases. Like many labs, we have demand for GPU time that far exceeds supply. Based on submitted workloads, at any moment in time we have outstanding requests for 2-3x more GPUs than are available. One way to think about this is that every available GPU hour on our cluster has 2-3 different research workloads competing for it.  Historically, we used a priority-based scheduler, and we allowed workloads to opt out of preemptability. Each team had a limit on concurrent GPUs that could be used by workloads which were protected from preemption. Preemptible workloads could exceed that limit on idle GPUs. This strategy produced predictable pathologies.