Multi-Objective Predictive Computing for Generative AI: Jointly Optimizing Computational Cost, Latency, Energy Consumption, and Service Quality

The increasing deployment of generative artificial intelligence (AI) systems has introduced a complex computational optimization problem involving economic expenditure, inference latency, energy consumption, and service quality. Although existing inference optimization techniques have investigated individual objectives, including computational cost reduction, low-latency serving, energy-efficient execution, and response quality preservation, these objectives are frequently optimized independently or through reactive resource allocation after computational demand has materialized. Emerging predictive computing approaches provide an opportunity to anticipate future inference requirements and prepare computational resources before actual requests arrive. However, predictive preparation alone does not establish whether the resulting computational decisions are optimal across multiple operational objectives. This paper introduces Multi-Objective Predictive Computing for Generative AI (MOPC-GAI), a conceptual framework that extends predictive computational orchestration toward the joint optimization of computational cost, service latency, energy consumption, and service quality. The proposed framework predicts future request distributions, computational requirements, GPU availability, energy prices, execution conditions, and service-level demands, and translates these predictions into anticipatory computational decisions. Its architecture integrates predictive workload modeling, multi-objective resource allocation, proactive inference preparation, energy-aware scheduling, latency-sensitive execution, quality-aware computation selection, and adaptive decision-making. A unified mathematical formulation is developed to characterize the relationships among four principal objectives: minimizing total computational expenditure, minimizing user-perceived response latency, minimizing energy consumption, and maximizing service quality. The formulation distinguishes between conflicting objectives, operational constraints, and decision variables, and introduces a prediction-aware multi-objective optimization mechanism for selecting computational actions under uncertainty. Particular attention is given to the trade-offs between off-peak computation and response freshness, low-cost execution and service latency, energy reduction and inference quality, and advance preparation and the risk of unused computational work. A Pareto-based decision framework is proposed to identify non-dominated computational strategies without assuming that a single operational configuration is universally optimal. The framework further investigates how predictive computing can coordinate local GPU resources, cloud inference instances, advance response generation, compatible computational-state preparation, dynamic computational budgets, and quality-dependent inference configurations. An experimental methodology is established to compare predictive multi-objective optimization against reactive inference, single-objective optimization, weighted multi-objective optimization, and conventional predictive resource scheduling. Evaluation metrics include total monetary cost, end-to-end latency, energy consumption, quality-adjusted service throughput, prediction uncertainty, and service-level compliance. The proposed research does not claim that all four objectives can always be simultaneously minimized or maximized. Rather, it investigates how predictive information can improve the selection of computational strategies under competing operational requirements. By integrating future demand prediction with multi-objective decision-making, MOPC-GAI establishes a theoretical foundation for generative AI infrastructures that anticipate not only what computation will be required, but also how that computation should be performed to balance economic efficiency, responsiveness, energy sustainability, and service quality.