Predictive Model Warm-Up for Generative AI: Forecasting Future Model Invocation and Proactively Preloading Model Weights

The increasing deployment of generative artificial intelligence (AI) systems has created inference infrastructures that simultaneously manage multiple large language models (LLMs), image generation models, speech models, multimodal models, and specialized fine-tuned model variants. In such environments, computational demand is determined not only by the number and complexity of incoming requests but also by which models are likely to be invoked and whether their parameters are already available in the required computational environment. Loading a model on demand may introduce substantial initialization latency associated with checkpoint retrieval, weight deserialization, data transfer, quantization processing, GPU memory allocation, kernel initialization, and inference engine preparation. Although existing model-serving systems investigate model caching, weight preloading, model placement, and predictive GPU prewarming, the decision of which model should be prepared, when preparation should occur, and where model weights should reside remains an important resource coordination problem. This paper introduces Predictive Model Warm-Up for Generative AI (PMWU-GAI), a conceptual framework that investigates the prediction of future model invocation and the proactive preparation of corresponding model weights before actual requests arrive. Unlike conventional model loading mechanisms that primarily respond to incoming invocation requests, PMWU-GAI estimates the probability, expected frequency, and anticipated time of future model usage from historical invocation patterns, conversational context, workflow dependencies, user interaction signals, external information trends, and scheduled computational activities. The framework translates these predictions into model-specific preparation decisions, including checkpoint retrieval, weight preloading, GPU memory residency, CPU memory staging, storage-layer caching, quantization preparation, and inference-engine warm-up. The proposed architecture integrates six interconnected functions: predictive model invocation forecasting, model dependency and workload identification, model-loading cost estimation, resource-aware preloading selection, multi-tier model residency management, and adaptive model activation and eviction. A mathematical optimization formulation is developed to maximize expected cold-start latency reduction and computational readiness while accounting for GPU memory constraints, model-loading expenditure, storage bandwidth, prediction uncertainty, model switching costs, energy consumption, and online inference service-level requirements. The framework further distinguishes between model artifact caching, model weight preloading, GPU-resident model preparation, and complete inference-engine warm-up, recognizing that loading weights alone does not necessarily eliminate all cold-start overhead. The research is positioned in relation to predictive computing, multi-model GPU prewarming, serverless LLM serving, and modern model-loading infrastructure. Rather than claiming that predictive preloading or model warm-up is an entirely unexplored concept, this study investigates a broader model-specific prediction and orchestration framework that connects anticipated model invocation with economically and operationally constrained preparation decisions. An experimental methodology is established to evaluate prediction accuracy, model readiness, cold-start latency, time-to-first-token, GPU memory utilization, model switching overhead, energy consumption, and total serving cost. The framework does not assume that speculative model loading is universally beneficial; its effectiveness depends on invocation predictability, preparation lead time, resource availability, loading costs, and the probability that prepared models will actually be used. Its practical effectiveness remains subject to empirical validation.