Meta details engineering behind doubling training efficiency (MFU) of its GEM ads recommendation foundation model while scaling training compute 4x over 12 months

Meta AI Research published a technical blog post detailing engineering improvements to GEM, its ads recommendation foundation model. The company reports raising end-to-end training efficiency (Model FLOPs Utilization) from roughly 10-12% to 20-25%, while scaling total training compute 4x over the past 12 months. The gains came from custom kernels (Jagged Flash Attention, GDPA, BlockAttention), ultra-low-precision training (MXFP8), and topology-aware 5D parallelism with network and memory co-design.

This is internal infrastructure powering ads ranking on Instagram and Facebook, not a product or public release, so the direct impact is Meta's own cost efficiency and ability to scale GEM further. For outside engineers and researchers, it offers directional lessons on adapting LLM-style infrastructure (parallelism, low-precision training) to sparse, embedding-heavy recommendation workloads, which is a genuinely hard and under-documented problem. Competitors and infra teams at other large recommendation-system operators (e.g. other ad platforms, e-commerce, search) are the most likely to act on this.

All efficiency figures are self-reported by Meta with no external benchmark, code release, or third-party reproduction, so the 2x MFU claim should be treated as a company-approved number, not an independently verified one. No code, weights, or detailed ablations are released, so "how Meta doubled efficiency" is more a high-level engineering narrative than something others can replicate directly.

1. 01Whether Meta or others publish a peer-reviewed paper or open-source reference implementation of Jagged Flash Attention, GDPA, or BlockAttention
2. 02Any measurable change in Meta's ads infrastructure costs or capital expenditure guidance in upcoming earnings calls that analysts tie to training efficiency gains
3. 03Whether competing platforms (Google, Amazon, TikTok/ByteDance, Pinterest) publish similar recommendation-model infrastructure disclosures in the next 6-12 months, suggesting an industry-wide shift
4. 04Any follow-up Meta disclosure quantifying downstream effects, such as ad relevance, revenue per user, or GEM model size increases enabled by the efficiency gains