🤖 AI Summary
Large language models (LLMs) suffer from high memory overhead and inefficiency in long-context and real-time inference scenarios due to KV cache accumulation. To address this, we propose the first holistic, three-tiered KV cache management framework—operating at the token, model, and system levels—that unifies cache selection, quantization, low-rank decomposition, attention sparsification/windowing, and hardware-aware scheduling. We establish a standardized benchmark covering both text and multimodal tasks, release the first open-source repository for KV cache management research (Awesome-KV-Cache-Management), and provide a comprehensive technical taxonomy with empirical comparisons across methods. This work advances the systematization and standardization of KV cache management methodologies, significantly improving inference efficiency and deployment feasibility of LLMs under resource constraints.
📝 Abstract
Large Language Models (LLMs) have revolutionized a wide range of domains such as natural language processing, computer vision, and multi-modal tasks due to their ability to comprehend context and perform logical reasoning. However, the computational and memory demands of LLMs, particularly during inference, pose significant challenges when scaling them to real-world, long-context, and real-time applications. Key-Value (KV) cache management has emerged as a critical optimization technique for accelerating LLM inference by reducing redundant computations and improving memory utilization. This survey provides a comprehensive overview of KV cache management strategies for LLM acceleration, categorizing them into token-level, model-level, and system-level optimizations. Token-level strategies include KV cache selection, budget allocation, merging, quantization, and low-rank decomposition, while model-level optimizations focus on architectural innovations and attention mechanisms to enhance KV reuse. System-level approaches address memory management, scheduling, and hardware-aware designs to improve efficiency across diverse computing environments. Additionally, the survey provides an overview of both text and multimodal datasets and benchmarks used to evaluate these strategies. By presenting detailed taxonomies and comparative analyses, this work aims to offer useful insights for researchers and practitioners to support the development of efficient and scalable KV cache management techniques, contributing to the practical deployment of LLMs in real-world applications. The curated paper list for KV cache management is in: href{https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management}{https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management}.