Reproduce “AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU” on OpenResearch
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About this paper
Yuning Zhang, Yan Yan, Nan Yang, Dong Yuan
AgentServe introduces an algorithm-system co-design to efficiently serve agentic AI workloads on a single consumer-grade GPU by integrating a dynamic, TPOT-driven scheduler with fine-grained GPU partitioning via CUDA Green Contexts. This approach mitigates head-of-line blocking from heterogeneous agent requests, resulting in up to 2.8x lower Time-To-First-Token (TTFT) and up to 2.7x more stable Time-Per-Output-Token (TPOT) compared to existing LLM serving systems.
Key insights
- Agentic AI workloads exhibit distinct, heterogeneous phases (cold prefills, resume prefills, short decodes) that necessitate specialized serving strategies beyond general-purpose LLM inference.
- Head-of-Line (HoL) blocking, where compute-intensive prefills delay latency-sensitive decodes, is a critical bottleneck for agentic AI on single consumer-grade GPUs.
- An algorithm-system co-design, combining dynamic feedback-driven scheduling with fine-grained GPU resource partitioning, can effectively mitigate HoL blocking and achieve stable low-latency performance for heterogeneous workloads on constrained hardware.
Summary by alphaXiv.
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