Why High GPU Allocation Does Not Mean High Utilization in AI Clusters
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…
Gang scheduling failures: Why AI Training Wastes GPUs
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…
GPU Resource Starvation: Why 100% Allocation Can Still Mean 15% Utilization
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,…
Priority Blindness: Why Your Most Expensive GPUs Keep Running the Wrong Jobs
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…