4 papers
AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU
Yuning Zhang, Yan Yan, Nan Yang +1
Large language models (LLMs) are increasingly deployed as AI agents that operate in short reasoning-action loops, interleaving model computation with external calls. Unlike traditi…
Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?
Xuanyu Chen, Nan Yang, Shuai Wang +1
The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about t…
Optimal Look-back Horizon for Time Series Forecasting in Federated Learning
Dahao Tang, Nan Yang, Yanli Li +3
Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in the federated learning scenarios where data is decentra…
DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
Yuning Zhang, Grant Pinkert, Nan Yang +2
Large Language Models (LLMs) are increasingly deployed as Internet/Web services (LLM-as-a-Service) with strict latency Service-Level Objectives (SLOs) under tight GPU memory budget…