Electricity-Price- and GPU-Availability-Aware Predictive Generative Inference Scheduling: A Cost-Optimized Framework for Dynamic Resource Forecasting, Off-Peak Precomputation, and Low-Cost Cloud GPU Allocation

The increasing deployment of Large Language Models (LLMs) has created substantial computational expenditure associated with model inference, particularly in systems that serve large numbers of users and maintain predictive response caches. Although predictive generative inference caching can reduce redundant online generation by preparing responses before actual user requests arrive, the computational expenditure associated with precomputation remains an important optimization challenge. Generating every predicted response immediately may consume expensive GPU resources during periods of high utilization, when real-time inference requests compete for computational capacity. Similarly, executing precomputation without considering electricity prices, cloud GPU pricing, and resource availability may substantially reduce the economic benefits of predictive inference caching. This paper proposes Electricity-Price- and GPU-Availability-Aware Predictive Generative Inference Scheduling (EPGAS), a predictive resource-management framework designed to minimize the economic cost of advance LLM computation through dynamic scheduling across time periods and computational environments. The central idea is to separate the decision of which computational tasks should be prepared from the decision of when and where those tasks should be executed. Building upon trend-aware predictive generative inference caching, the proposed framework receives predicted future questions and their associated precomputation tasks, estimates future GPU workloads and computational resource prices, and dynamically schedules eligible tasks during periods when GPU capacity is relatively available and computation is economically advantageous. The proposed architecture integrates five principal mechanisms: GPU workload forecasting, off-peak precomputation scheduling, dynamic electricity-price awareness, low-cost cloud GPU instance selection, and adaptive predictive task scheduling. A unified optimization model considers expected computational savings, electricity expenditure, GPU rental costs, task deadlines, resource availability, migration overhead, and prediction uncertainty. Unlike conventional predictive caching approaches that primarily focus on predicting future information demand and preparing reusable responses, EPGAS focuses on the economic execution of those preparation tasks. It investigates whether predicted generative inference workloads can be shifted toward more favorable computational periods and environments without compromising response availability or system reliability. The paper develops the conceptual architecture, mathematical optimization formulation, scheduling algorithm, experimental design, and evaluation metrics. It also discusses practical constraints involving GPU workload prediction errors, cloud instance interruptions, electricity-price volatility, scheduling delays, and precomputation deadlines. The proposed framework introduces a research direction for economically optimized predictive generative computing. Its effectiveness remains subject to empirical validation under realistic GPU workloads, electricity pricing conditions, and cloud infrastructure environments.