AI · This text was generated with artificial intelligence
Inference fleets face a fundamental operational challenge: they can be globally constrained while meaningful pockets of capacity remain locally idle, according to engineering data from Meta. Scaling infrastructure to meet peak demand reliably results in idle inference capacity due to standard operational mismatches. Peak-provisioning and demand-variation mismatch occurs because inference fleets must provision for peak demand while traffic fluctuates by time of day and season, leaving necessary capacity temporarily unused during lower-demand periods.
Additional inefficiencies stem from regional resource-ratio mismatches where supporting resources like CPUs do not match accelerator availability, alongside accelerator-type mismatch across heterogeneous hardware generations and vendors. These structural realities create an industry-wide paradox where hardware sits idle locally despite global constraints. At Meta's scale, these temporary pockets represent substantial unmonetized compute.
Unlocking Opportunistic Capacity Through Elasticity Levers
To capture value from fragmented reservations, Meta built a system that enhances ads recommendations by leveraging idle inference capacity in the fleet. Because this opportunistic capacity is preemptible, volatile, and carries no long-term guarantees, utilizing it requires high workload elasticity, sustained quality at scale, and limited cross-service dependencies.
Physical and logical isolation protects the capacity reserved for baseline serving at Meta. Two elasticity levers in the system allow ads recommendation work to expand across opportunistic capacity, without compromising baseline performance. Rank More Ads (RMA) adjusts the number of candidates evaluated by the ranking stages, while Serve Better Models (SBM) adjusts the share of requests routed to a more powerful model.
RMA increases the number of candidates evaluated by the ranking stages when opportunistic capacity is available.
Separate feedback loops control RMA candidate volume and SBM model coverage. When capacity supply shifts dynamically, value-aware load shedding at both servers and clients allows the system to degrade gracefully when capacity is suddenly reclaimed.
Translating Technical Efficiency Into Advertiser Value
Turning otherwise idle capacity into active compute alters the unit economics of ad delivery without requiring additional hardware procurement cycles. By pooling fragmented hardware allocations into a shared opportunistic resource, the infrastructure generates higher relevance for users and better outcomes for advertisers without requiring additional capacity orders. The architecture demonstrates that infrastructural efficiency depends as much on workload elasticity and real-time load shedding as it does on raw silicon availability. Extracting margin from dormant assets replaces the old capital expenditure playbook with algorithmic resource arbitrage. Meta has effectively proven that software-level elasticity can squeeze millions out of hardware that would otherwise burn power while sitting empty between traffic spikes. For any enterprise running large-scale inference, this shifts the strategic focus from buying more silicon to wringing every ounce of margin out of the compute you already own.