By dividing large training runs across decoupled 'islands' of compute, with asynchronous data flowing between them, this architecture isolates local disruptions so that other parts of the system can keep learning efficiently.
大多数人认为分布式AI训练需要高度同步和紧密耦合的系统才能保证效率，但作者认为通过解耦的'计算岛屿'架构，即使局部硬件故障，系统其他部分仍能高效学习，因为故障被隔离了。这挑战了传统分布式训练必须保持同步的主流认知。