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cs.DC2026
TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing
Zhuohang Bian, Feiyang Wu, Chengrui Zhang +3
Multi-agent LLM applications organize execution in synchronized rounds where a central scheduler gathers outputs from all agents and redistributes the combined context. This All-Ga…
cs.DC2025
TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications
Zhuohang Bian, Feiyang Wu, Zhuoran Li +2
Large Language Models (LLMs) are increasingly deployed in complex multi-agent applications that rely on external function calls. This workload creates severe performance challenges…
cs.DC2025
TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems
Feiyang Wu, Zhuohang Bian, Guoyang Duan +6
The increasing demand for large language model (LLM) serving has necessitated significant advancements in the optimization and profiling of LLM inference systems. As these models b…