Preble: Efficient Distributed Prompt Scheduling for LLM Serving
Vikranth Srivatsa Zijian He Reyna Abhyankar Dongming Li Yiying Zhang
Prompts to large language models (LLMs) have evolved beyond simple user questions.
For LLMs to solve complex problems, today’s practices are to include domain-specific
instructions, illustration of tool usages, and/or long context such as textbook chapters in
prompts. As such, many parts of prompts are repetitive across requests. Recent works
propose to cache and reuse KV state of prompts. However, they are all confined to a single-
GPU optimization, while production LLM serving systems are distributed by nature.

This paper proposes Preble, the first distributed LLM serving platform that targets and op-
timizes for prompt sharing. We designed a distributed scheduling system that co-optimizes
KV state reuse and computation load-balancing with a new scheduling algorithm and a
hierarchical scheduling mechanism. Our evaluation of Preble with real workloads and re-
quest arrival patterns on two open-source LLMs shows that Preble outperforms the SOTA
serving systems by 1.5× to 14.5× on average latency and 2× to 10× on p99 latency.
LLM prefix caching, LLM serving, Distributed systems for ML
This paper proposes Preble, the first distributed LLM serving platform that targets prompt sharing and improves SOTA serving systems by up to 14.5× on average latency.