For teams running AI workloads on Kubernetes, a fully allocated GPU pool and a growing batch-job queue might make expanding the pool seem like the obvious next step. But a growing queue could point to timing, serving capacity, or the choice of model and hardware. Before expanding a GPU pool, teams should define what success looks like, when it must finish, and what it costs to deliver. “An allocated GPU, a busy GPU, and a workload producing valuable results are not the same thing.” That starts with a distinction: an allocated GPU, a busy GPU, and a workload producing valuable results are not...

Source: The New Stack