GPU utilisation shows compute activity, not whether capacity is actually available. A GPU can report low utilisation while remaining fully allocated to one workload. This post explains why workloads queue beside idle-looking GPUs, how allocation, memory, placement, and application bottlenecks contribute, and how GPU sharing methods differ in their tradeoffs.

AI workloads can wait for GPUs even when monitoring shows idle compute. A GPU with low compute activity is not necessarily available capacity for another workload because allocation rules, memory requirements, hardware compatibility, isolation, and placement constraints determine what can actually run. The post should explain how to distinguish an actual hardware shortage from an allocation, placement, or application bottleneck. Use the 5% average GPU utilisation finding from Cast AI’s 2026 report as a research hook, but do not imply that queued workloads and idle GPUs occurred together in the same measured environments or that 95% of GPU capacity is immediately reclaimable.

GPU utilisation is a phrase that covers at least four different measurements, and conflating them is the fastest route to a misdiagnosis. What most dashboards surface is compute utilisation: the percentage of streaming multiprocessors (SMs) that are active in a given time window. That number tells you how busy the silicon is when it runs. It tells you nothing about whether the device is available for a new workload.

A model loaded into VRAM occupies that memory continuously. The inference service may answer one request per minute, keeping compute activity at 5%, but the device is allocated, the memory is occupied, and Kubernetes will not schedule anything else on it.

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