The field of deep learning is moving towards more efficient and accurate memory management, with a focus on predicting GPU memory requirements and optimizing resource scheduling. Researchers are exploring innovative approaches, such as integrating bidirectional gated recurrent units with Transformer architectures and leveraging CPU-only dynamic analysis to estimate peak GPU memory requirements. These advancements have the potential to significantly improve the utilization efficiency of computing clusters and prevent out-of-memory errors. Noteworthy papers include: xMem, which proposes a novel framework for accurate estimation of GPU memory requirements, and Jenga, which presents a tiered memory system that maximizes accesses to fast memory tiers while avoiding thrashing. These developments are expected to have a significant impact on the field, enabling more efficient and effective deep learning workflows.