The majority of AI training is currently conducted in hyperscale data centers, with a few large data centers processing the bulk of the workload. However, as inference and daily tasks become more prevalent, there is a growing opportunity for decentralized GPU networks to play a significant role. This shift indicates a potential opening for smaller scale GPU networks to handle a portion of the processing load, diversifying the landscape of AI computation.