Storage Efficient

Storage-efficient training methods for large-scale machine learning models are crucial for addressing the escalating data storage demands of increasingly complex models. Current research focuses on techniques like near-storage processing to mitigate bandwidth bottlenecks and on representing data using compact representations such as vector-quantized tokens, significantly reducing storage needs while maintaining high performance. These advancements are vital for enabling the training of larger, more powerful models in resource-constrained environments and facilitating broader access to advanced AI technologies.