Decentralized GPU networks are pitching themselves as a lower-cost layer for running AI workloads, while training the latest models remains concentrated inside hyperscale data centers. Frontier AI training involves building the largest and most advanced systems, a process that requires thousands of GPUs to operate in tight synchronization. That level of coordination makes decentralized networks impractical for top-end AI training, where internet latency and reliability cannot match the tightly coupled hardware in centralized data centers. Most AI workloads in production do not resemble large-scale model training, opening space for decentralized…
What Role Is Left for Decentralized GPU Networks in AI?
