精选理由
Meta分享了他们搞定AI训练存储的实战经验，不是理论，适合关心规模化和成本优化的工程师看。
过去几年，模型能力和训练数据集规模呈指数级增长，前沿模型发布间隔从几个月缩短到几周。Meta分享了其为大规模AI训练设计的存储蓝图，强调可靠快速的存储对降低计算成本和加速训练至关重要。该方案涉及分布式文件系统与高性能硬件结合。
原文 · Meta Engineering Blog
Meta’s AI Storage Blueprint at Scale
Over the past several years, model capabilities and training dataset sizes have experienced exponential growth. During the past year or so, the time between new-frontier-model releases has gone down from months to weeks. Reliable and fast access to storage is important to both the speed and computational cost of this AI innovation. If AI is [...] Read More... The post Meta’s AI Storage Blueprint at Scale appeared first on Engineering at Meta .