Predictive GPU Cluster Sizing for Generative AI: Forecasting Future Computing Node Requirements Before Demand Materializes

The increasing deployment of generative artificial intelligence (AI) services has created growing requirements for scalable GPU computing infrastructure. Large language models (LLMs), multimodal generation systems, and distributed inference platforms frequently experience fluctuating computational demand, making the determination of appropriate GPU cluster sizes an important infrastructure management problem. Conventional GPU cluster management approaches generally rely on current resource utilization, observed workload queues, predefined autoscaling thresholds, or reactive capacity adjustment. Although these approaches can respond to changing demand, they may introduce scaling delays, insufficient capacity during sudden workload increases, and unnecessary infrastructure expenditure during periods of reduced activity. Furthermore, predicting GPU utilization alone does not necessarily determine how many physical or virtual GPU computing nodes should be provisioned, particularly when workloads involve heterogeneous models, different GPU memory requirements, distributed inference, and node-level placement constraints. This paper introduces Predictive GPU Cluster Sizing for Generative AI (PGCS-GAI), a conceptual framework that investigates whether future GPU cluster requirements can be estimated and translated into proactive computing node allocation decisions before corresponding workloads materialize. Rather than focusing exclusively on forecasting GPU utilization or computational demand, the proposed framework predicts the number, configuration, and temporal availability of GPU nodes required to satisfy anticipated generative AI workloads. Its architecture integrates predictive workload modeling, multi-horizon GPU capacity forecasting, node-level resource requirement estimation, heterogeneous cluster configuration analysis, predictive scale-up and scale-down decisions, and uncertainty-aware capacity management. The framework considers multiple workload characteristics, including anticipated inference request volumes, prompt-processing requirements, autoregressive generation demand, model deployment configurations, GPU memory consumption, concurrent execution requirements, and distributed inference dependencies. These signals are translated into estimates of future GPU computational capacity and subsequently mapped to discrete node-count decisions under hardware compatibility, memory capacity, scheduling, availability, and service-level constraints. A unified optimization formulation is developed to minimize the combined cost of GPU infrastructure provisioning, under-capacity risks, excessive idle resources, scaling operations, and resource reservation while maintaining appropriate service reliability. The framework further distinguishes between predicting aggregate GPU demand and determining the actual cluster topology required to accommodate that demand. An experimental methodology is established to evaluate prediction accuracy, node-count estimation error, scaling lead time, infrastructure utilization, provisioning expenditure, request latency, capacity shortage, and operational reliability. Comparisons with reactive autoscaling, threshold-based scaling, utilization forecasting, and conventional predictive resource allocation are proposed. The framework does not assume that predictive cluster sizing is universally advantageous; its effectiveness depends on workload predictability, infrastructure provisioning latency, hardware heterogeneity, forecast uncertainty, and the economic consequences of incorrect capacity decisions. By extending predictive computing from anticipated inference preparation and resource orchestration to the proactive determination of future GPU cluster sizes, this research investigates a further dimension of generative AI infrastructure optimization: determining not only what computational resources will be needed, but also how many computing nodes should be available before future computational demand arrives.