Predictive GPU Resource Reservation for Generative AI: Forecast-Driven Capacity Commitment and Uncertainty-Aware Accelerator Availability Management

The increasing deployment of generative artificial intelligence (AI) systems has created growing demand for high-performance graphics processing units (GPUs), particularly for large language model (LLM) inference, multimodal generation, and computationally intensive AI services. Although predictive computing provides a mechanism for anticipating future computational demand, and economic-aware predictive computing investigates when and where computation should be performed at lower cost, neither predicting future workloads nor selecting economical execution opportunities necessarily guarantees that the required GPU capacity will be available when those workloads materialize. GPU availability is increasingly influenced by shared infrastructure demand, hardware scarcity, regional capacity constraints, cloud provider allocation policies, reservation lead times, and competing workloads. Consequently, generative AI services may encounter resource shortages even when their future computational requirements are reasonably predictable. This paper introduces Predictive GPU Resource Reservation for Generative AI (PGRR-GAI), a conceptual framework that investigates how predicted future computational demand can be translated into proactive GPU capacity reservation decisions. Instead of waiting until an inference workload arrives and subsequently attempting to acquire available accelerators, the proposed framework estimates future GPU requirements and determines whether selected computational capacity should be reserved, partially reserved, or obtained through conventional on-demand provisioning. Its architecture integrates predictive workload forecasting, GPU capacity requirement estimation, reservation opportunity evaluation, uncertainty-aware reservation sizing, reservation timing optimization, capacity availability assessment, and adaptive reservation management. A central component of PGRR-GAI is a multi-period optimization formulation that determines the appropriate amount of GPU capacity to reserve under uncertain future demand. The formulation considers predicted GPU requirements, reservation costs, expected utilization, unused reserved capacity, resource shortage penalties, provisioning delays, reservation lead times, cancellation flexibility, and service-level requirements. It further distinguishes between capacity reservation and actual GPU utilization: reserving an accelerator provides a mechanism for securing future resource availability, but does not necessarily mean that the accelerator must immediately execute computational workloads. This distinction allows reserved capacity to support subsequent inference, predictive precomputation, model loading, and other computational operations without conflating resource availability with resource execution. The framework also introduces confidence-aware reservation strategies, including conservative reservation, probabilistic reservation, rolling-horizon reservation, and hybrid reservation combining guaranteed capacity with flexible on-demand resources. An experimental methodology is established to compare these strategies against reactive provisioning, fixed-capacity reservation, and conventional demand-based autoscaling. Evaluation metrics include reservation prediction accuracy, capacity utilization, reservation waste, resource shortage probability, provisioning delay, total GPU expenditure, request latency, and service-level objective violations. Importantly, PGRR-GAI does not assume that advance reservation is universally beneficial. Its effectiveness depends on demand predictability, GPU capacity availability, reservation flexibility, procurement cost, and the economic consequences of both insufficient and excessive reservation. The proposed research extends predictive computing from anticipating future computational workloads to proactively securing the physical accelerator capacity required to execute them, establishing a framework for uncertainty-aware capacity management in increasingly resource-intensive generative AI infrastructures.