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

• This post describes the next generation of Meta Andromeda, Meta’s personalized ads retrieval engine, and how co-designing the model, system, and GPU hardware unlocks a new efficiency frontier for ads retrieval.
• In production, the system scores every eligible ad with near ranking-level model performance at retrieval scale and surfaces the top candidates at over one billion ads per second, a new throughput benchmark for ads retrieval-stage models (i.e., a 100x lift compared to the 2024 Meta Andromeda system).
• For advertisers, this means better performance automatically from the retrieval system matching ads across a larger candidate pool, within the same latency budget. Since 2025, Meta Andromeda has delivered a 2.4% increase in conversions on Facebook.

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