Compute governance proposals often rely on the assumption that frontier AI training requires large, detectable computing clusters. However, recent advances in distributed training algorithms could allow developers to conduct frontier-scale training on distributed agglomerations of hardware, rather than needing large datacenter facilities. Developers who prefer not to be constrained by regulations may structure their hardware in a manner that evades the registration and monitoring requirements associated with compute governance. Therefore, regulations must be designed to detect and prevent illicit distributed training operations. This paper evaluates the feasibility of such evasion and outlines recommended countermeasures, including whistleblowing, chip tracking, forensic accounting, and memory and compute thresholds for clusters.

翻译：算力治理提案通常依赖于一个假设：前沿人工智能训练需要大规模、可检测的计算集群。然而，分布式训练算法的最新进展可能允许开发者利用分布式硬件聚合，而非依赖大型数据中心设施，进行前沿规模的训练。倾向于不受监管约束的开发者可能以规避算力治理相关注册与监控要求的方式构建其硬件体系。因此，监管措施必须设计为能够检测并阻止非法的分布式训练操作。本文评估了此类规避行为的可行性，并概述了推荐的应对措施，包括举报机制、芯片追踪、法务会计以及集群内存与算力阈值设定。