GPU Capacity Scarcity: A Prioritization Framework for Allocating Constrained AI Compute across Competing Enterprise Initiatives

Abstract

Enterprise AI programs have reached a point at which the accelerator, not the model or the data pipeline, is the binding constraint on strategic delivery, and GPU compute now consumes a share of technical budgets large enough that allocation decisions are indistinguishable from portfolio decisions. Yet the research response has split into two camps that rarely meet: systems-level GPU schedulers that optimize cluster utilization over workloads they assume are already approved, and project-portfolio methods that rank initiatives against generic resources but have never been specialized to accelerator-class compute at sub-hour granularity. This paper argues that GPU scarcity is now a portfolio-management problem, not only a scheduling problem, and proposes a five-stage prioritization framework that composes LSTM-based demand forecasting, Analytic Hierarchy Process criteria weighting, composite priority scoring, priority-weighted resource-constrained multi-project scheduling, and Kubernetes-level admission enforcement with bounded-wait guarantees. We evaluate the framework through discrete-event simulation across three enterprise portfolio scenarios and three oversubscription levels, benchmarked against FIFO and a utilization-maximizing genetic-algorithm baseline. Strategic-value-weighted throughput improved by 31% over FIFO and 21% over the utilization baseline while cluster utilization stayed within three percentage points, and allocation decisions remained stable under ±15% weight perturbation.