Predictive GPU Memory Fragmentation Management for Generative AI: Forecasting Future Allocation Fragmentation and Anticipating Computational Resource Waste

The increasing deployment of large language models (LLMs) and other generative artificial intelligence (AI) applications has introduced increasingly complex GPU memory allocation patterns. Modern generative AI workloads involve heterogeneous memory requirements associated with model parameters, intermediate activations, attention operations, key-value (KV) caches, temporary tensors, batching, and concurrent inference requests. Although existing GPU memory management techniques employ caching allocators, memory pooling, dynamic allocation, and specialized KV cache management mechanisms to improve memory utilization, the resulting memory layout may become fragmented as allocation and deallocation operations accumulate over time. Consequently, the total amount of available GPU memory does not necessarily represent the amount of memory that can be effectively allocated to future computational tasks. This paper introduces Predictive GPU Memory Fragmentation Management (PGMFM), a conceptual framework that investigates whether future GPU memory fragmentation and associated resource waste can be anticipated before subsequent allocation requests materialize. Unlike conventional GPU memory demand forecasting, which primarily estimates future memory consumption, PGMFM focuses on predicting the future spatial distribution of allocated and available memory, the formation of unusable memory regions, allocation-size incompatibility, and the probability of future allocation failures. Its architecture integrates allocation-history monitoring, workload-aware fragmentation prediction, future memory-layout estimation, allocation feasibility forecasting, predictive memory reorganization, and adaptive resource management. A central component is a fragmentation-aware optimization formulation that distinguishes total free memory from effectively allocatable memory and estimates the expected computational cost of future fragmentation. The framework further considers the interaction between concurrent inference requests, variable-length KV caches, dynamic batching, memory retention, tensor lifetimes, and heterogeneous allocation sizes. A resource-waste prediction model is developed to characterize memory that remains physically available but cannot be effectively utilized under anticipated allocation requirements. An experimental methodology is proposed to compare predictive fragmentation management with conventional memory allocation, reactive memory defragmentation, memory pooling, and workload-aware allocation strategies. The proposed research does not assume that all forms of GPU memory fragmentation can be eliminated or that predictive reorganization is universally beneficial. Its practical effectiveness depends on workload predictability, allocator behavior, memory relocation constraints, execution overhead, and the availability of compatible memory-management mechanisms. By extending predictive computing from forecasting memory capacity requirements to anticipating future memory-layout inefficiencies, this research establishes a conceptual foundation for proactive GPU memory organization in generative AI infrastructure.