LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues

Understand

Multi-turn dialogues are essential in many real-world applications of large language models, such as chatbots and virtual assistants.

• As conversation histories become longer, existing large language models face increasing computational and memory challenges, which hinder their ability to provide efficient and responsive interactions.
• Most current acceleration methods either compress the context or optimize key value caching, but they often rely on fixed or position-based heuristics that do not adapt well to the dynamic and unpredictable patterns found in actual multi-turn conversations.
• As a result, these models cannot accurately identify and prioritize the most relevant context, leading to degraded response quality.