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

Dharma-AI describes a constraint-aware GPU allocation system designed to improve enterprise AI cluster utilization and priority-weighted output compared with a FIFO scheduler that reserves peak real-time inference capacity and schedules other jobs by arrival order. Across seven benchmark scenarios using identical hardware and workloads, the allocator increased utilization by up to 33 percentage points and improved priority-weighted value in every case, by as much as 105%, while matching utilization but raising value by 15.9% in a 64-GPU scale test. The system schedules training, batch inference, quantization, and elastic real-time inference together over a planning horizon, accounting for contiguous GPU requirements, non-preemption, real-time demand fluctuations, GPU reassignment limits, and job priorities. A fast heuristic produces valid allocation plans in roughly 1–15 milliseconds, while an optional formal optimization mode can refine those plans offline. Its approach relies on workload-specific demand forecasting and a rolling 24-hour optimization process that commits only the current timestep and re-runs every 30 to 60 minutes, aiming to adapt to changing conditions without interrupting running work.

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