Meta 利用闲置推理算力提升广告推荐效果
Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations
Meta 把闲置 GPU 算力“榨”出来跑广告推荐，还讲了两条弹性设计的具体做法，做推理调度的工程师可以直接抄作业。
Meta 在官方工程博客介绍了如何把数据中心里的闲置 AI 推理算力用于广告推荐模型。系统通过两个弹性杠杆（elasticity levers）让广告推荐任务扩展到机会性算力（opportunistic capacity）上，同时不牺牲基线性能。文章详细说明了该系统在 Meta 规模下的设计思路与日常运维方式。
Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations
At Meta, we’ve built a system that enhances ads recommendations by leveraging idle inference capacity in our fleet. Two elasticity levers in the system allow ads recommendation work to expand across opportunistic capacity, without compromising baseline performance. This post describes how we designed and now operate the system at Meta’s scale and how it drives [...] Read More... The post Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations appeared first on Engineering at Meta .
