# GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model

DevFeed: [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model](<https://devfeed.tech/articles/gem-training-how-meta-doubled-the-efficiency-of-its-llm-scale-ads-foundation-model-127.md>)

Original publisher: [Read original article](<https://engineering.fb.com/2026/08/03/ml-applications/training-gem-at-llm-scale-meta-ads-recommendation-foundation-model/>)

Author: Darren Liu; Huayu Li; Raghav Boinepalli; Yuzhen Huang; Jackie (Jiaqi) Xu; Richard Qiu; Chunzhi Yang; Rich Zhu; Dev (Devashish) Shankar; Huaqing Xiong

Published: 2026-08-03T18:00:17Z

Content type: article

Language: en

Sources: [Engineering at Meta](<https://devfeed.tech/sources/engineering-at-meta.md>), [Meta AI Research](<https://devfeed.tech/sources/meta-ai-research.md>), [Meta ML Applications](<https://devfeed.tech/sources/meta-ml-applications.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [networking](<https://devfeed.tech/topics/networking.md>)

Tags: [ads](<https://devfeed.tech/tags/ads.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [llm](<https://devfeed.tech/tags/llm.md>), [meta](<https://devfeed.tech/tags/meta.md>), [ml-applications](<https://devfeed.tech/tags/ml-applications.md>), [networking](<https://devfeed.tech/tags/networking.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

Meta describes training its GEM ads recommendation foundation model at LLM scale. The article covers recommendation-specific kernels, ultra-low-precision training, and topology-aware parallelism that doubled end-to-end training efficiency to 20-25% MFU while increasing training FLOPs fourfold.

## Source excerpt

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 [...] Read More... The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared first on Engineering at Meta.