Same Cluster, 33 Points More Utilization: What Changed Was the Order

Utilization, not intelligence, appears to be a major constraint in enterprise AI systems, according to the previous post. No playbook has yet emerged for a mature GPU Management practice. This report presents a constraint-aware GPU allocator and benchmarks its performance against a FIFO scheduler across seven benchmark scenarios.

The results show that the new allocator can increase GPU utilization by up to 33 percentage points and priority-weighted output by up to 105% on identical hardware running identical workloads. The key difference lies in the order of allocation decisions, not the hardware itself. The allocator optimizes the assignment of GPUs to jobs, considering the type of workload and the priority of each job.

It treats real-time inference as a demand-driven process, allocating resources based on each timestep's demand rather than reserving a fixed pool. Batch-like jobs, such as training and batch inference, are assigned to GPUs in the best available slots, prioritizing high-priority jobs throughout the scheduling horizon. The new allocator removes the inefficiencies introduced by fixed reservations and arrival-order placement, recovering unused capacity and increasing overall utilization.

The results demonstrate that the allocator can significantly improve utilization and output quality without any changes to the underlying hardware.