Fast Distributed Inference Serving for Large Language Models
Bingyang Wu Yinmin Zhong Zili Zhang Gang Huang Xuanzhe Liu Xin Jin
Large language models (LLMs) power a new generation of interactive AI
applications exemplified by ChatGPT. The interactive nature of these
applications demand low job completion time (JCT) for model inference. Existing
LLM serving systems use run-to-completion processing for inference jobs, which
suffers from head-of-line blocking and long JCT. We present FastServe, a
distributed inference serving system for LLMs. FastServe exploits the
autoregressive pattern of LLM inference to enable preemption at the granularity
of each output token. FastServe uses preemptive scheduling to minimize JCT with
a novel skip-join Multi-Level Feedback Queue scheduler. Based on the new semi
information-agnostic setting of LLM inference, the scheduler leverages the
input length information to assign an appropriate initial queue for each
arrival job to join. The higher priority queues than the joined queue are
skipped to reduce demotions. We design an efficient GPU memory management
mechanism that proactively offloads and uploads intermediate states between GPU
memory and host memory for LLM inference. We build a system prototype of
FastServe based on NVIDIA FasterTransformer. Experimental results show that
compared to the state-of-the-art solution Orca, FastServe improves the average
and tail JCT by up to 5.1$\times$ and 6.4$\times$, respectively.