Meta’s EB-Scale AI Storage Architecture
A report on Meta’s “Meta’s AI Storage Blueprint at Scale” examines its BLOB Storage architecture for AI training, including simplified metadata, direct client I/O, and multi-tier caching. It also compares Meta’s design with JuiceFS, which the report promotes. Meta’s reported 80% cache-hit rate and 93% average and 97% worst-case reductions in data-loading time were not independently verified in the supplied material.
The architecture provides a reference for teams planning large-scale AI training storage, particularly on metadata simplification, direct client access, multi-tier caching and cross-region data access.
当 AI 存储进入超大规模，架构会朝什么方向演进？
Meta 最近发布的《Meta's AI Storage Blueprint at Scale》提供了一个难得的参考样本。Meta 运营着总规模达数百 EB 级存储集群，文章重点介绍了其面向 AI 训练的 BLOB Storage 架构。
在这一规模下，数据访问路径上的延迟会被进一步放大：一次慢请求就可能让大量 GPU 等待，跨地域读取也会拖慢模型迭代。Meta 的应对思路可以概括为：简化元数据结构、缩短数据读取路径，并把缓存推近计算。
本文将选取 Meta 博客中的关键设计，梳理其 AI 存储架构的变化，再对照同样具备 EB 级生产实践的 JuiceFS 架构，希望为正在规划或建设大规模数据存储系统的团队提供架构参考。
01 为什么存储对 AI 工作负载如此重要
在深入讨论架构之前，我们有必要先理解：为什么存储已经成为 AI 基础设施中如此关键的一环。