Impactful scheduling for GPU clusters

Ai2 Deploys New GPU Scheduler to Maximize Research Impact

The Allen Institute for AI (Ai2) has implemented a new scheduling system for its large-scale NVIDIA GPU clusters, directly addressing the chronic problem of resource contention in high-demand AI labs. The system replaces a legacy priority-based queue, which led to resource "squatting" and inefficiency, with a model based on GPU time budgets and fair-share allocation. This strategic shift is designed to maximize research impact by ensuring that compute resources are aligned with institutional priorities, not just immediate demand.

Budgets and Contracts Replace Queues

Facing demand that was consistently two to three times its supply, Ai2’s infrastructure team found its previous priority-based system was creating pathologies like "priority inflation," where all critical workloads were set to high, and significant on-call toil for engineers. Their solution applies principles from economics to manage its fleet of H100, B200, and B300 GPUs. The new architecture is built on three core pillars:

• GPU Time Budgets: Instead of fighting for scheduling priority, research leads are allocated a guaranteed share of total GPU time, turning compute allocation into a transparent, administrative budgeting process.
• Hierarchical Fair-Share: An algorithm tracks GPU usage over a seven-day lookback window, prioritizing workloads from under-utilized budgets to ensure every project receives its allocated share over time, while still allowing teams to burst beyond their limits on idle capacity.
• The Scheduling Contract: Each job declares a "minimum runtime" to make progress. During this period it is protected, but afterward, it can be preempted and automatically re-queued. This enables time-slicing and prevents any single job from monopolizing hardware indefinitely.

From Operational Toil to Strategic Allocation

The results of the new system are tangible. By automating preemption and re-queuing based on the scheduling contract, Ai2 reduced the need for manual engineer intervention in hardware maintenance by 74%. The model also provides more flexibility. According to researcher Chris Clark, the ability to reclaim unused time makes it feel like the team has "an extra 30% compute." For the broader AI industry, Ai2’s model provides a blueprint for managing scarce compute assets, shifting the focus from maximizing raw occupancy to ensuring the most valuable work gets done.

By treating GPU time as a budgeted asset rather than a first-come, first-served resource, Ai2 has transformed its cluster management from a reactive technical problem into a proactive strategic planning process, ensuring its most valuable compute resources are directed towards its most impactful research.