GPU-verbindingen zijn cruciaal voor efficiënte gedistribueerde training van AI-modellen

Distributed training performance depends equally on GPU interconnection quality and optimization strategy, with approaches like DDP, FSDP, and ZeRO stages each offering different tradeoffs for large-scale model training. Physical hardware configuration proves as critical as software choices when scaling machine learning workloads across multiple processors.