Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations

What happened

Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations, Meta Engineering announced. Two elasticity levers in the system allow ads recommendation work to expand across opportunistic capacity, without compromising baseline performance. The post Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations appeared first on Engineering at Meta .

Inference fleets face a capacity paradox: They can be globally constrained while meaningful pockets of capacity remain locally idle. 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.