AI training is dominated by hyperscale data centers, but inference and everyday workloads are opening real space for decentralized GPU networks.
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.
