Predictive Memory Preparation for AI Computing: Forecasting Future Data Access and Proactively Allocating Multi-Tier Memory Resources

The increasing complexity of artificial intelligence (AI) workloads has created substantial memory-management challenges across modern computing infrastructures. Deep learning models, large language models (LLMs), and multimodal generative systems frequently require large quantities of model weights, intermediate tensors, activation values, attention-related states, datasets, and temporary computational buffers. Although modern computing systems provide multiple memory-management mechanisms, including demand paging, dynamic memory allocation, tensor swapping, memory migration, and data prefetching, many operations remain reactive: memory resources are allocated and data are transferred primarily when computational execution approaches the point at which those resources are required. This operational paradigm can introduce memory allocation delays, page faults, data-transfer stalls, GPU memory pressure, and unnecessary synchronization between computational stages. This paper introduces Predictive Memory Preparation for AI Computing (PMP-AIC), a conceptual framework that investigates whether future memory requirements can be anticipated from predicted computational workloads and proactively prepared before actual memory accesses occur. Rather than treating memory access as the exclusive trigger for resource allocation and data movement, PMP-AIC considers future execution plans, historical tensor-access patterns, computational dependency graphs, model execution stages, and workload characteristics as signals for predicting future memory requirements. Its architecture integrates predictive memory-demand modeling, tensor and data-access forecasting, multi-tier memory allocation, proactive data migration, predictive GPU memory preparation, adaptive memory eviction, and uncertainty-aware resource scheduling. The framework considers multiple memory levels, including storage devices, host RAM, GPU VRAM, and, where supported, remote or pooled memory resources. A unified optimization formulation is developed to determine which memory resources should be allocated, which data objects should be prefetched, where they should be placed, and when they should be released. The formulation accounts for memory capacity, transfer latency, allocation overhead, bandwidth contention, prediction uncertainty, data reuse probability, and computational execution deadlines. An important research objective is to overlap memory preparation with ongoing computation, potentially reducing memory-related waiting time and improving end-to-end execution latency. The framework further distinguishes between memory reservation, physical allocation, data prefetching, tensor migration, and memory reclamation, recognizing that these operations have different computational and resource implications. An experimental methodology is proposed to evaluate prediction accuracy, prefetch hit rate, memory preparation latency, page-fault frequency, data-transfer overhead, memory utilization, execution time, and end-to-end workload latency. The framework does not assume that predictive memory preparation is universally beneficial; its effectiveness depends on access predictability, memory availability, data-transfer characteristics, preparation overhead, and the ability to coordinate preparation with actual computational execution. By extending predictive computing from anticipating computational workloads to anticipating the memory resources required by those workloads, this research proposes a memory-centric direction for improving the responsiveness and resource efficiency of increasingly complex AI computing infrastructures.