The RINAS project researches techniques to enable modern AI to be trained using fewer computer resources. Large Language Models (LLMs) have significant computer resource demands, and all modern AI applications across different areas and with various data types will exhibit similar demands on the underlying cyberinfrastructure. AI training is unusually sensitive to the performance of the data center disks on which training data is stored. This is due to the large data sizes, which prevent in-memory storage and necessitate training involving many iterative epochs, during which the full dataset must be read and processed. The project will use and extend an open-source data formatting and compression system developed by the Apache Foundation. Furthermore, it will utilize traditional optimizations, such as parallel computing (where multiple jobs run simultaneously), and modifications to the AI (Artificial Intelligence) models that accelerate training without significantly compromising model accuracy. AI itself will be utilized to enhance the performance of these jobs further. The new software developed will be deployed on supercomputers run by the National Science Foundation (NSF) and the Department of Energy. It will allow the development of new AI models that will benefit the nation across both industry and academia. The project will collaborate with the AI development and use community through three alliances, each having over 100 organizational members from industry, academia,