Meta 发布 Intelligent Delivery：让广告排序系统的每一份算力用在刀刃上
Intelligent Delivery: Making Every Unit of Compute Serve Better Ads
Meta 工程博客这篇讲广告排序怎么动态分配算力，把每个请求该用多大模型、跑在哪种芯片都算清楚。做推荐系统和推理优化的工程师能直接抄思路，比单纯加卡省钱多了。
Meta 工程团队在官方博客发布 Intelligent Delivery 系统，解决广告排序模型向 LLM 规模扩张时算力供给不足的问题。核心观察是：同样一单位算力，投放到不同广告请求、不同模型、不同芯片上，对整体系统效果的贡献差异很大。
该系统的做法是把算力分配当作一个全局优化问题来求解，模型不再以固定配置服务所有流量，而是根据请求的价值动态决定使用多大的模型、在哪类芯片上执行，从而把稀缺算力集中到对广告效果影响最大的环节。
对行业而言，这是推理算力经济性的一个典型案例：与其无限堆 GPU，不如先榨干已有硬件的边际收益。对自建广告或推荐系统的团队，这套按请求动态分配推理资源的思路可以直接借鉴。
Intelligent Delivery: Making Every Unit of Compute Serve Better Ads
Ads ranking models are growing toward LLM scale faster than the hardware to run them can keep up. An extra unit of compute has more impact on some ad requests, models, and chips than on others. The challenge is deciding where to allocate it to get the best outcome for the entire system. We built [...] Read More... The post Intelligent Delivery: Making Every Unit of Compute Serve Better Ads appeared first on Engineering at Meta .
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Meta, Intelligent Delivery, 推理优化, 广告系统.
Primary source verification: published directly by Meta Engineering Blog with first-party authority.
