Google ignites efficiency turbo – storage requirements of AI models to decrease drastically

Alphabet has introduced a series of new algorithms aimed at significantly reducing the storage requirements of large AI models. The focus is on technologies like TurboQuant, Quantized Johnson-Lindenstrauss, and PolarQuant, which have been specifically developed for applications such as Large Language Models and semantic search systems. The goal is to lower the growing infrastructure costs in the AI sector while maintaining the performance of the models. New algorithms tackle core problems of AI With TurboQuant, Google brings a more efficient vector...