![[2602.10016] Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.10016] Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
Summary
The paper 'Kunlun' presents a unified architecture for massive-scale recommendation systems, addressing scaling laws and resource allocation to enhance efficiency and performance.
Why It Matters
As recommendation systems become increasingly critical in various applications, understanding and improving their scaling laws is essential for optimizing resource use and enhancing user experience. This research provides insights into efficient architecture design that can significantly impact production systems, particularly in advertising.
Key Takeaways
- Identifies poor scaling efficiency as a barrier to predictable power-law scaling in recommendation systems.
- Introduces Kunlun, a scalable architecture that improves model efficiency and resource allocation.
- Demonstrates low-level optimizations like Generalized Dot-Product Attention and Hierarchical Seed Pooling.
- Achieves a significant increase in Model FLOPs Utilization from 17% to 37% on NVIDIA B200 GPUs.
- Kunlun is deployed in Meta Ads models, showcasing its practical production impact.
Computer Science > Information Retrieval arXiv:2602.10016 (cs) [Submitted on 10 Feb 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design Authors:Bojian Hou, Xiaolong Liu, Xiaoyi Liu, Jiaqi Xu, Yasmine Badr, Mengyue Hang, Sudhanshu Chanpuriya, Junqing Zhou, Yuhang Yang, Han Xu, Qiuling Suo, Laming Chen, Yuxi Hu, Jiasheng Zhang, Huaqing Xiong, Yuzhen Huang, Chao Chen, Yue Dong, Yi Yang, Shuo Chang, Xiaorui Gan, Wenlin Chen, Santanu Kolay, Darren Liu, Jade Nie, Chunzhi Yang, Ellie Wen, Jiyan Yang, Huayu Li View a PDF of the paper titled Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design, by Bojian Hou and 28 other authors View PDF HTML (experimental) Abstract:Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-scale recommendation systems. While such laws are established for large language models, they remain challenging for recommendation systems, especially those processing both user history and context features. We identify poor scaling efficiency as the main barrier to predictable power-law scaling, stemming from inefficient modules with low Model FLOPs Utilization (MFU) and suboptimal resource allocation. We introduce Kunlun, a scalable architecture that systematically ...
