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Meta uses idle inference capacity for ads recommendations without hurting baseline performance
Meta’s system expands recommendation work onto opportunistic inference capacity using two elasticity levers while protecting baseline performance. It offers a practical example of extracting more value from an inference fleet without compromising its primary workload.
- What
- Meta’s ads recommendation system aggregates locally idle inference capacity into a shared pool for optional recommendation work.
- How
- Rank More Ads (RMA) increases ranking candidates, while Serve Better Models (SBM) routes more requests to a stronger model; isolation and load shedding protect baseline serving.
- Why it matters
- The elasticity levers let recommendations use changing spare capacity while preserving baseline latency and reliability.
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