Utilization-aware scheduling allocates GPUs based on real usage, not requests alone. It combines five capabilities: utilization tracking, atomic gang scheduling, full-resource co-scheduling, business-driven priority, and hardware health gates. Together, these…
Priority blindness happens when a GPU cluster schedules jobs by arrival time instead of business value. A low-priority experiment can occupy 16 GPUs all weekend while a revenue-critical retraining job…
GPU resource starvation is one of the most expensive problems in AI infrastructure. Dashboards may show 100% GPU allocation. However, real utilization can sit near 15% when CPUs, storage, memory,…
Gang scheduling failures waste GPUs when distributed AI jobs start before all workers are ready. The fix is simple: enforce all-or-nothing scheduling so all required workers start together. As a…
Your dashboard says your GPUs are busy. Your training pipeline may tell a different story. Many teams trust allocation numbers. If a cluster shows 90% or 95% allocation, it looks…