The GPU Shortage Inside Your Own Infrastructure: Why AI Workloads Queue While Capacity Sits Idle
ainews ·
This article explains why AI workloads often queue despite monitoring systems showing low GPU utilization, clarifying that compute activity metrics do not indicate available capacity. Kubernetes defaults to exclusive allocation, meaning a single workload holding a device blocks all others regardless of how little silicon it actively uses. The text identifies four primary causes for this phenomenon: exclusive device holding, VRAM occupancy by loaded models, placement rule constraints, and application-layer bottlenecks such as CPU or I/O delays. It compares three GPU sharing methods—time-slicing, Multi-Instance GPU (MIG), and Multi-Process Service (MPS)—highlighting their distinct tradeoffs regarding memory isolation, hardware requirements, and observability. A diagnostic sequence is provided to help teams identify whether the issue stems from allocation, memory, placement, or external dependencies before purchasing new hardware. The article cites a Cast AI report noting average fleet-wide GPU utilization of just 5%, emphasizing that most idle-looking capacity is actually allocated rather than free. Finally, it presents a case study where ALLEN Digital reduced costs by 71% by shifting from dedicated instances to shared GPU configurations using automation tools.
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