According to Google DeepMind on X, the team successfully trained a 12B Google Gemma model across four US regions over low-bandwidth networks and demonstrated heterogeneous training across TPU6e and TPUv5p without performance regressions. As reported by Google DeepMind, this cross-region, low-bandwidth orchestration suggests large language model training can be decoupled from single datacenters, enabling cost-efficient multi-region capacity pooling, improved resiliency, and better utilization of stranded compute. According to Google DeepMind, the ability to mix TPU generations without slowdown opens procurement flexibility and reduces upgrade friction for enterprises planning phased hardware refreshes.