Meta Adaptive Ranking Model: Bending the Inference Scaling Curve to Serve LLM-Scale Models for Ads
3/31/2026 · Xi Chen, Yuxin Chen, Zeliang Chen, Jonathan Herbach, Jian Jiao, Jinfu Leng, Dianshi Li, Gaoxiang Liu, Liang Luo, GP Musumeci, Michael Shao, Ellie Wen, Ruichao Xiao, Yavuz Yetim, Nancy Yu, Zhengkai Zhang
What this post added
This post introduces the Meta Adaptive Ranking Model, a significant advancement in scaling AI recommendation systems for ads to LLM-scale complexity. It addresses the inference trilemma by employing intelligent request routing, inference-efficient model scaling (request-centric architecture, Request-Oriented Optimization, Wukong Turbo), deep model-system co-design (selective FP8 quantization, hardware-aware graph and kernel specialization), and a reimagined serving infrastructure (trillion-parameter scale, multi-card GPU serving). This innovation has resulted in a +3% increase in ad conversions and +5% increase in ad click-through rate on Instagram.