What role is left for decentralized GPU networks in AI?
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.
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