Summary of A Survey on Large Language Model Acceleration Based on Kv Cache Management, by Haoyang Li et al.
A Survey on Large Language Model Acceleration based on KV Cache Management
by Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang, Zhanchao Xu, Xuejia Chen, Nicole Hu, Wei Dong, Qing Li, Lei Chen
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have significantly impacted various domains, including natural language processing, computer vision, and multi-modal tasks. However, their computational and memory demands during inference pose challenges when scaling them to real-world applications. Key-Value (KV) cache management has emerged as a crucial optimization technique for accelerating LLM inference by reducing redundant computations and improving memory utilization. This survey categorizes KV cache management strategies into token-level, model-level, and system-level optimizations. Token-level strategies include KV cache selection, budget allocation, merging, quantization, and low-rank decomposition. 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. The survey also provides an overview of text and multimodal datasets and benchmarks used to evaluate these strategies. By presenting detailed taxonomies and comparative analyses, this work aims to offer insights for researchers and practitioners to develop efficient and scalable KV cache management techniques, contributing to the practical deployment of LLMs in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand language and do logic. They’re great at things like text recognition and image analysis. But, they use a lot of computing power and memory, which makes it hard to use them in everyday life. To fix this, people came up with an idea called Key-Value (KV) cache management. This helps LLMs by reducing extra work and using memory more efficiently. The paper talks about different ways to do KV cache management and how they can be used together. It also shows examples of text and images that were used to test these methods. |
Keywords
» Artificial intelligence » Attention » Inference » Multi modal » Natural language processing » Optimization » Quantization » Token
