Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations

• At Meta, we’ve built a system that enhances ads recommendations by leveraging idle inference capacity in our fleet.
• Two elasticity levers in the system allow ads recommendation work to expand across opportunistic capacity, without compromising baseline performance.
• This post describes how we designed and now operate the system at Meta’s scale and how it drives significant advertiser value and capacity efficiency.

Inference fleets face a capacity paradox: They can be globally constrained while meaningful pockets of capacity remain locally idle. At Meta, we built a system that aggregates these pockets into a shared opportunistic capacity pool and turns them into better ads recommendations. By putting otherwise idle capacity to work, the system helps people see more relevant ads and improves advertiser outcomes without requiring additional capacity orders.

Using this opportunistic capacity safely is difficult. It’s distributed across team-owned capacity allocations, regions, and accelerator types, and may be reclaimed with little notice. The ads inference system must therefore adapt workload as capacity supply changes without overloading the dedicated capacity that serves baseline workload.

We address this challenge with two complementary elasticity levers. Rank More Ads (RMA) adjusts the number of candidates evaluated by the ranking stages. Serve Better Models (SBM) adjusts the share of requests routed to a more powerful model.