Matryoshka: Realizing Hyperscale Data Center Network Design for the AI Era#

Yan Cai, Jialong Li (Max Planck Institute for Informatics), Kutalmis Akpinar, Tianxiang Li, Hany Morsy, Jason Wilson, Sunil Khaunte (Meta), Yiting Xia (MPI), Ying Zhang (Meta), et al.

• Why: The gap this paper names — between logical DCN designs and the concrete switch configurations that realize them — is exactly the "last mile" problem anyone running multi-generation fabric automation knows. It's also the clearest public account yet of how a hyperscaler's DCN tooling strains under AI-cluster growth.
• What: Meta's production DCN design system, operational for 6+ years, that compiles high-level design intent (physical connectivity, addressing, routing policy) into working switch configurations. It has guided the design of nearly 900 DCs across 18 distinct types, including Meta's latest 100K-GPU training supercluster.
• How: Intent-based, model-driven compilation: DCN designs are expressed as machine-readable models and systematically compiled down to per-switch configs, so design, addressing, and routing evolve together and heterogeneous cluster types are treated as first-class variants rather than snowflakes.
• Where applicable: Meta-specific production experience, but the architecture pattern (model-driven intent-to-config compilation for rapidly evolving, heterogeneous AI fabrics) generalizes to any operator building multi-generation DCNs. The 6-year production track record and ~900-DC scale make the claims firmly substantiated.