Economic-Aware Predictive Computing for Generative AI: Coordinating GPU Demand Forecasting, Off-Peak Precomputation, Dynamic Electricity Pricing, and Low-Cost Cloud Resource Allocation

The increasing deployment of generative artificial intelligence (AI) systems has introduced substantial computational expenditure associated with large language model (LLM) inference, particularly under fluctuating user demand and heterogeneous GPU resource availability. Although predictive computing provides a potential mechanism for anticipating future inference requirements and preparing reusable computational resources before actual requests arrive, predicting future demand alone does not necessarily ensure economically efficient computation. The timing, location, and financial cost of advance computation are equally important. Performing predictive computation during periods of high GPU utilization or expensive electricity may reduce its economic attractiveness, even when the predicted workloads are sufficiently accurate. This paper introduces Economic-Aware Predictive Computing for Generative AI (EAPC-GAI), a framework that extends predictive computational preparation through coordinated GPU workload forecasting, off-peak precomputation, dynamic electricity price awareness, cloud GPU price selection, and adaptive computational task scheduling. The proposed framework estimates future inference demand and GPU utilization, identifies computational opportunities during periods of relatively low workload and favorable resource prices, and dynamically determines whether predicted workloads should be executed locally, deferred to a lower-cost time interval, or assigned to alternative cloud GPU instances. Its architecture integrates workload forecasting, resource availability prediction, electricity price estimation, cloud instance price evaluation, cost-aware task selection, interruption-aware execution, and reusable computational resource management. A unified optimization formulation is developed to minimize total computational expenditure while accounting for prediction uncertainty, GPU execution cost, electricity consumption, cloud instance pricing, task deadlines, interruption risks, storage requirements, and online inference service-level constraints. Importantly, the framework distinguishes between the economic cost of electricity consumption and the commercial price of cloud GPU instances, recognizing that these costs may exhibit different temporal and geographical characteristics. An experimental methodology is established to evaluate whether economic-aware predictive computation can reduce total inference-related expenditure compared with reactive inference, conventional predictive preparation, and resource-unaware precomputation. The proposed framework does not assume that advance computation is universally economical; rather, it investigates the conditions under which computational workloads can be strategically transferred from expensive or congested periods to periods offering lower resource costs and greater available capacity. Its practical effectiveness remains subject to empirical validation.