Rina Diane Caballar

AI training consumes enormous energy, prompting researchers and companies to explore decentralized training as a near-term solution. Rather than concentrating compute in massive data centers, decentralization distributes model training across independent nodes, allowing computation to leverage existing energy sources like solar-powered homes or idle servers. This approach requires both hardware coordination across geographically dispersed clusters and algorithmic innovations like federated learning, though challenges around communication costs and fault tolerance remain active areas of research.