Predictive Computing for Large Language Models: A Unified Framework for Future Demand Forecasting, Proactive Resource Preparation, and Cost-Aware Computational Optimization

The increasing deployment of Large Language Models (LLMs) has created substantial computational challenges involving GPU utilization, memory consumption, inference latency, energy expenditure, and cloud infrastructure costs. Existing LLM serving systems primarily optimize computational resources in response to incoming requests or use historical workload information to determine resource allocation. Although these approaches improve inference efficiency, an important opportunity remains: computational resources, model states, and inference tasks may be prepared before future demand actually materializes. This paper proposes Predictive Computing for Large Language Models (PCLLM), a unified conceptual framework that extends predictive generative inference caching toward a broader predictive computational management paradigm. Rather than focusing exclusively on predicting future questions and generating reusable answers, PCLLM investigates the systematic prediction and optimization of future computational demand, resource requirements, model execution states, task outcomes, and computational benefits. The proposed framework integrates seven research layers: (1) future user demand and behavioral prediction; (2) predictive computational resource management; (3) predictive model and inference-state preparation; (4) predictive task scheduling and execution management; (5) predictive economic and energy optimization; (6) uncertainty-aware reliability and adaptive control; and (7) predictive computing evaluation and decision theory. A central component is the proposed Predictive Computing Decision Engine (PCDE), which evaluates future computational opportunities by jointly considering predicted demand, resource availability, GPU utilization, memory requirements, execution costs, electricity prices, carbon intensity, prediction uncertainty, and expected computational savings. The framework introduces a unified optimization formulation for deciding whether a computational task should be executed in advance, when and where it should be executed, which resources should be allocated, and whether the expected benefits justify the additional expenditure. It also establishes mechanisms for predictive model prewarming, KV cache preparation, response precomputation, resource reservation, workload migration, task cancellation, and real-time inference protection. Unlike frameworks limited to proactive response caching or individual resource optimization mechanisms, PCLLM conceptualizes predictive computing as a coordinated decision-making architecture spanning the entire LLM serving lifecycle. The paper develops the conceptual architecture, mathematical formulation, operational mechanisms, research hypotheses, experimental methodology, and potential application directions. It also discusses the relationship between PCLLM and existing research on predictive workload management, proactive GPU prewarming, semantic caching, energy-aware inference scheduling, and KV cache reuse. No experimental superiority is claimed. The proposed framework represents a research architecture whose practical effectiveness, economic benefits, and scalability require systematic empirical validation.