External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation.

@inproceedings{liang-2025-external, author = {Mingfu Liang and Xi Liu and Rong Jin and Boyang Liu and Qiuling Suo and Qinghai Zhou and Song Zhou and Laming Chen and Hua Zheng and Zhiyuan Li and Shali Jiang and Jiyan Yang and Xiaozhen Xia and Fan Yang and Yasmine Badr and Ellie Wen and Shuyu Xu and Hansey Chen and Zhengyu Zhang and Jade Nie and Chunzhi Yang and Zhichen Zeng and Weilin Zhang and Xingliang Huang and Qianru Li and Shiquan Wang and Evelyn Lyu and Wenjing Lu and Rui Zhang and Wenjun Wang and Jason Rudy and Mengyue Hang and Kai Wang and Bo Long and Wenlin Chen and Santanu Kolay and Huayu Li}, editor = {Guodong Long and Michale Blumestein and Yi Chang and Liane Lewin-Eytan and Zi Huang and Elad Yom-Tov}, title = {{External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation}}, booktitle = {{Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025 - 2 May 2025}}, pages = {344--353}, publisher = {ACM}, year = {2025}, url = {https://doi.org/10.1145/3701716.3715223}, doi = {https://doi.org/10.1145/3701716.3715223} }