Oral Thu, May 21, 2026 • 2:15 PM – 2:30 PM PDT

Scaling Up Large Language Models Serving Systems for Semantic Job Search

Kayhan Behdin ⋅ Qingquan Song ⋅ Sriram Vasudevan ⋅ Jian Sheng ⋅ Xiaojing Ma ⋅ Zhengze Zhou ⋅ Chuanrui Zhu ⋅ Guoyao Li ⋅ Chanh Nguyen ⋅ ⋅ Hejian Sang ⋅ Ata Fatahi ⋅ ⋅ Xiaoqing Wang ⋅ Qing Lan ⋅ ⋅ Qi Guo ⋅ Caleb Johnson ⋅ Zhipeng Wang ⋅

Abstract

Large Language Models (LLMs) have demonstrated impressive quality when applied to predictive tasks such as relevance ranking and semantic search. However, deployment of such LLMs remains prohibitively expensive for industry applications with strict latency and throughput requirements. In this work, we present lessons and efficiency insights from developing a purely text-based decoder-only Small Language Model (SLM) for a semantic search application at LinkedIn. Particularly, we discuss model compression techniques such as pruning that allow us to reduce the model size by up to 40% while maintaining the accuracy. Additionally, we present context compression techniques that allow us to reduce the input context length by more than 10x with minimal loss of accuracy. Finally, we present practical lessons from optimizing the serving infrastructure for deploying such a system on GPUs at scale, serving millions of requests per second. Taken together, this allows us to increase our system’s throughput by 10x in a real-world deployment, while meeting our quality bar.