Reclaiming Idle GPUs in Kubernetes Before They Burn Your Budget
Last month I finally looked at our GPU utilization dashboards properly. What I saw made me physically uncomfortable: 14 A100 GPUs across our cluster, average utilization hovering around 15%. We were paying for dedicated hardware that spent most of its time doing absolutely nothing. This is embarrassingly common. Teams request a full GPU for a workload that uses it for training bursts of 20 minutes, then idles for hours. Kubernetes treats GPUs as integer resources — you either have one or you don’t. There’s no native way to share. ...