Predictive Computational Budget Optimization for Generative AI: Selecting the Highest-Value Precomputation Tasks Under Fixed Resource Constraints

The increasing deployment of generative artificial intelligence (AI) systems has introduced substantial computational requirements associated with large language model (LLM) inference, proactive response generation, KV cache preparation, model-state initialization, and other predictive computational operations. Emerging predictive computing approaches investigate whether future computational demand can be anticipated and partially prepared before actual user requests materialize. However, even when future demand can be predicted with reasonable accuracy, the available computational resources for advance preparation remain limited. A system cannot necessarily prepare every predicted workload, particularly when GPU capacity, execution time, energy consumption, storage availability, and financial expenditure are constrained. Consequently, an important unresolved problem concerns how a predictive computing system should determine which anticipated computational tasks deserve priority when its preparation budget is fixed. This paper introduces Predictive Computational Budget Optimization (PCBO), a conceptual framework for selecting the highest-value predictive computation tasks under predetermined resource constraints. Rather than attempting to maximize the number of prepared workloads or independently minimizing the execution cost of each task, PCBO evaluates candidate tasks according to their expected future computational benefits, successful reuse probabilities, preparation costs, resource requirements, prediction uncertainty, and potential contribution to overall service efficiency. Its central objective is to maximize the expected net computational benefit obtainable from a limited preparation budget. The framework integrates predictive workload valuation, computational benefit estimation, budget-constrained task selection, multi-resource allocation, marginal utility analysis, uncertainty-aware optimization, and adaptive budget redistribution. A unified mathematical formulation is developed in which candidate precomputation tasks are represented by decision variables associated with expected reusable inference savings and heterogeneous resource consumption. The framework further distinguishes between independently valuable tasks and groups of tasks whose preparation costs or future benefits exhibit computational dependencies, including shared contextual prefixes, reusable KV states, and overlapping response-generation requirements. Both deterministic and uncertainty-aware optimization formulations are considered. A practical experimental methodology is proposed to compare PCBO against popularity-based selection, probability-based selection, cost-based ranking, greedy benefit-to-cost selection, and resource-unaware predictive preparation. Evaluation criteria include total computational expenditure, realized computational savings, budget utilization, successful reuse, prediction errors, response quality, and marginal benefit per unit of allocated resources. The proposed research positions computational budget allocation as a distinct optimization problem within predictive computing for generative AI. Its central proposition is that the effectiveness of predictive computation depends not only on correctly anticipating future demand or reducing the price of advance execution, but also on selecting the most valuable computational preparation tasks under limited resources. The framework does not assume that its optimization strategy is universally superior; its effectiveness depends on demand predictability, benefit estimation accuracy, resource heterogeneity, task compatibility, and the characteristics of the available computational budget. Its practical advantages remain subject to empirical validation.