RAM-Aware Predictive Computing for Generative AI: Forecasting Future System Memory Demand and Proactively Preparing Host Memory Resources

The increasing deployment of generative artificial intelligence (AI) systems has introduced substantial demands on host system memory, particularly in large language model (LLM) inference environments involving model loading, CPU-based preprocessing, memory-intensive request handling, KV cache management, model offloading, and concurrent execution. Although existing memory management techniques primarily allocate, reclaim, and optimize system memory in response to current computational requirements, they generally provide limited consideration of the possibility of anticipating future host memory demand before corresponding workloads arrive. This paper introduces RAM-Aware Predictive Computing for Generative AI (RAM-PCGA), a framework that investigates the prediction of future system RAM requirements and the proactive preparation of host memory resources before anticipated computational workloads materialize. The proposed framework estimates future memory consumption using historical inference workloads, request arrival patterns, model characteristics, prompt lengths, expected generation lengths, concurrent execution levels, memory allocation histories, and anticipated model-loading activities. Its architecture integrates predictive RAM demand modeling, memory consumption decomposition, proactive memory reservation, adaptive allocation and reclamation, model-loading preparation, memory-aware workload scheduling, and uncertainty-aware memory protection. Unlike conventional memory monitoring, which primarily identifies memory pressure after it develops, RAM-PCGA attempts to estimate future memory requirements and determine appropriate resource management actions in advance. A unified optimization formulation is developed to minimize memory-related operational expenditure while accounting for prediction errors, memory reservation overhead, allocation latency, memory fragmentation, memory pressure, swapping costs, and out-of-memory risks. Particular attention is given to distinguishing host RAM from GPU VRAM, recognizing that CPU-side memory requirements and GPU device memory requirements exhibit different allocation mechanisms, utilization patterns, and optimization constraints. The framework also distinguishes between predicting aggregate memory consumption and predicting the individual memory components responsible for future demand, including model weights, CPU-resident KV caches, intermediate tensors, pinned memory, preprocessing buffers, runtime overhead, and reusable data structures. An experimental methodology is established to evaluate forecasting accuracy, peak memory prediction, allocation latency, memory utilization, swap activity, out-of-memory frequency, and overall inference efficiency. The proposed framework does not assume that proactive memory preparation is universally beneficial; rather, it investigates the conditions under which accurate memory forecasting and anticipatory resource management can improve the reliability and efficiency of generative AI infrastructure. Its practical effectiveness remains subject to empirical validation.