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About this paper
Chiheng Lou, Sheng Qi, Rui Kang, Yong Zhang, Chen Sun, Pengcheng Wang, Xuanzhe Liu, Xin Jin
WarmServe introduces a multi-LLM serving system that leverages workload predictability to proactively prewarm GPUs, allowing multiple model parameters to be loaded onto a single GPU for rapid instance creation. The system reduces cold-start latency for new LLM instances while improving resource utilization and throughput.
Key insights
- LLM serving workloads, despite short-term randomness, exhibit strong periodicity and long-term predictability, which can be effectively utilized for proactive resource allocation.
- Proactively prewarming GPUs with multiple model parameters ("one-for-many") can drastically reduce cold-start latency without the high resource overhead of dedicated backup replicas for each model.
- Careful orchestration of multi-GPU model placement and dynamic memory management is essential to overcome challenges like cross-model interference and transient prewarming windows, common in complex LLM serving environments.
Summary by alphaXiv.
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