Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design

Meta Andromeda is Meta's machine learning system for personalized ads retrieval, the first stage of the ads recommendation pipeline. Retrieval narrows a vast pool of eligible ads down to a small set of strong candidates in milliseconds; a later, more computationally expensive ranking stage then scores those survivors in fine detail. Historically, the deep modeling quality of the ranking stage has been too costly to apply at retrieval's scale.

Building on the retrieval foundation established with Andromeda's 2024 launch on the NVIDIA GH200 Grace Hopper Superchip, this post shares how co-designing the model, system, and hardware together brings near ranking-level performance to the retrieval scale. Challenges and Opportunities in Scaling Retrieval The 2024 Andromeda deployment reimplemented retrieval with an end-to-end optimized retrieval implementation from custom AI hardware with hierarchical indexing and GPU-native operators to a purpose-built ML model, growing model capacity by four orders of magnitude.