Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20–25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in 12 months, by co-designing kernels, precision, parallelism, networking, and memory together.
Training GEM presents unique engineering challenges at the intersection of recommendation systems and LLMs as the model combines a hybrid architecture plus recommendations-domain data properties that are unlike typical LLM workloads.
AI infrastructure optimized for LLM training (kernels, parallelism, low precision recipes etc.) does not directly transfer, requiring significant innovation and hardware/software co-design to reach LLM-scale training for recommendation models efficiently.
We tackled these challenges through complementary compute efficiency and scaling efficiency innovations:
Compute efficiency: Achieved through a customized recommendation kernel library — Jagged Flash Attention (JFA), Generalized Dot-Product Attention (GDPA), BlockAttention, etc. — and mixed ultra-low precision training (including MXFP8 attention and MLP) optimized for recommendation workloads, purpose-built to exploit latest generation GPU’s architecture.
