Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI

Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains. Building on this foundation, our next goal is to further scale both compute and intelligence transfer with next-generation hardware, and expand this capability across Meta’s entire ads ecosystem. This spans dozens of surfaces and objectives, delivering seamless full-funnel optimization across awareness, consideration, and conversions. 

The 2026 generation of GEM shifts from isolated per-domain optimizations — such as Facebook, Instagram, Reels — to a single central foundation model. It learns from data and signals across all of Meta’s ads platforms, bends the scaling curve at LLM-scale computation, and delivers strong advertiser outcomes through post-training techniques that provide a larger latency budget without requiring real-time evaluation of all ad candidates — all on a substantially more efficient training stack. The result is simultaneous ad performance gains across every surface and funnel stage, and deeper understanding that better matches ads to what people are looking for.

GEM has delivered 18.9% conversion improvement and 12% ad click-through rate (CTR) improvement across Meta’s ad surfaces and objectives, and additionally contributed 5.8%, 4.1%, and 1.6% conversion uplift on Facebook, Instagram, and off-site conversion for targeted users over the past year. These gains span both upper-funnel surfaces and deep-funnel optimization goals, driving stronger returns for advertisers.