Meta Adaptive Ranking Model: Bending the Inference Scaling Curve to Serve LLM-Scale Models for Ads

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.