Meta's Engineering at Meta post on August 3, 2026 says GEM, the foundation model behind Instagram and Facebook ads recommendations, now runs at LLM scale across several thousand latest-generation GPUs.
The post says Meta co-designed kernels, precision, parallelism, networking, and memory to double GEM's end-to-end efficiency to 20–25% Model FLOPs Utilization (MFU).
It also says training FLOPs scaled up 4x in 12 months under that same efficiency-focused effort.
The same writeup describes GEM as a hybrid system with trillions of sparse embedding parameters and billions of dense parameters, with recommendation data patterns like jagged user-history sequences making standard LLM training assumptions inefficient.
To address this, Meta says it built custom kernels such as Jagged Flash Attention, Generalized Dot-Product Attention, and BlockAttention, and added low-precision MXFP8 attention and MLP paths.
For distributed scale-out, Meta cites 5D parallel strategies across dense and sparse dimensions and topology-aware networking, including a multi-tier cluster view, to reduce communication bottlenecks while tracking both compute and scaling efficiency together.
