Google DeepMind unveiled Decoupled DiLoCo, a distributed training architecture for large AI models. It uses asynchronous fault-isolated learning units to maintain 88% utilization amid hardware failures, versus 27% for traditional methods. The system slashes cross-data center bandwidth needs to 0.84 Gbps, enables global training on commercial internet, and supports self-healing and heterogeneous hardware like different TPU generations.