In this paper, we propose DEEPSERVE, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DEEPSERVE addresses key challenges such as resource allocation, serving efficiency, and cold start latencies through four main design components. First, DEEPSERVE uses a simple serverless abstraction called the request-job-task model, which helps manage diverse AI workloads across posttraining and model-serving tasks. Second, DEEPSERVE integrates an in-house serving engine named FLOWSERVE using a microkernel-inspired design, NPU-centric execution, and SPMD-based parallelism to optimize LLM serving. Third, DEEPSERVE includes novel scheduling policies tailored for a configuration with both PD-disaggregated and PD-colocated instances. Fourth, DEEPSERVE includes optimizations such as pre-warmed pods, DRAM pre-loading, and NPU-fork, which allow DEEPSERVE to scale up to 64 instances in seconds. DEEPSERVE has been in production for over a year, operating on a large Ascend NPU cluster and providing industrystandard APIs for fine-tuning, agent serving, and model serving to our customers.
@article{arxiv.2501.14417,
title = {DeepServe: Serverless Large Language Model Serving at Scale},
author = {Junhao Hu and Jiang Xu and Zhixia Liu and Yulong He and Yuetao Chen and Hao Xu and Jiang Liu and Jie Meng and Baoquan Zhang and Shining Wan and Gengyuan Dan and Zhiyu Dong and Zhihao Ren and Changhong Liu and Tao Xie and Dayun Lin and Qin Zhang and Yue Yu and Hao Feng and Xusheng Chen and Yizhou Shan},
journal= {arXiv preprint arXiv:2501.14417},
year = {2025}
}