Revolutionizing LLM Inference: Adaptive Request Scheduling Boosts Throughput by 33.1x and Achieves 96.3% SLO Compliance

AugServe is a groundbreaking inference framework designed to enhance the efficiency of augmented large language models (LLMs) in web applications. Developed by researchers from Zhejiang University and Alibaba Group, AugServe tackles significant issues faced by existing systems, such as inefficient request scheduling and static batch processing that contribute to queuing delays. It employs a two-stage adaptive request scheduling strategy that prioritizes requests and dynamically adjusts token batching, resulting in an impressive performance boost of up to 33.1 times compared to competitors like vLLM and InferCept. The framework also reduces time-to-first-token by up to 96.3%, significantly improving user experience. AugServe addresses the challenges of first-come, first-served scheduling by minimizing head-of-line blocking and maximizing throughput, essential for meeting service-level objectives. Ultimately, AugServe optimizes resource utilization, ensuring better scalability and sustainability while delivering superior performance across various models and workload conditions. For further details, refer to the study on ArXiv.

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