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Efficient Memory Management for Large Language Models: xKV Compresses KV-Cache using SVD

Large language models (LLMs) with extensive context windows enable impressive applications, but place high demands on memory requirements, especially for storing key-value states (KV-cache). The size of this KV-cache grows linearly with the context length, making the application of LLMs with very long contexts on resource-constrained systems difficult. Various approaches to reducing memory requirements have already been explored, including merging the KV-cache of different layers. However, these methods often require extensive pre-training or are based on the assumption of high cosine similarity of token representations between layers, which is often not true in practice.

New research now introduces xKV, a method for compressing the KV-cache based on singular value decomposition (SVD). The core idea of xKV is based on the observation that the dominant singular vectors of the KV-cache match remarkably well across different layers. xKV leverages this insight by projecting the KV-cache of grouped layers into a shared low-dimensional subspace using SVD. This significantly reduces the size of the KV-cache without significantly impacting the performance of the language model.

In contrast to previous inter-layer compression methods, xKV does not require additional training and can be applied directly to pre-trained models. The evaluation of xKV on the RULER benchmark for long contexts with established LLMs like Llama-3.1 and Qwen2.5 shows promising results. xKV achieves compression rates of up to 6.8x compared to state-of-the-art methods while simultaneously improving accuracy by up to 2.7%.

Furthermore, xKV is compatible with the innovative Multi-Head Latent Attention (MLA) architecture used in models like DeepSeek-Coder-V2. Here, too, xKV achieves a considerable 3x compression of the KV-cache in programming tasks without degrading model performance. These results underscore the power and versatility of xKV in addressing memory bottlenecks in LLMs with long context windows.

The xKV method thus offers a promising approach to the efficient use of memory resources in large language models. By using SVD and exploiting similarities between layers, xKV enables a significant reduction of the KV-cache without compromising model accuracy. This opens up new possibilities for the use of LLMs with long context windows on resource-constrained systems and could drive the development of new applications in the field of artificial intelligence.

For companies like Mindverse, which specialize in the development and deployment of AI solutions, such advances in the efficient use of resources are of particular importance. Optimizing memory management makes it possible to run powerful LLMs even on less powerful systems, thus democratizing access to advanced AI technologies. The development of customized solutions, such as chatbots, voicebots, AI search engines, and knowledge systems, benefits directly from these innovations and enables the creation of more efficient and scalable AI applications.

Bibliography: Chang, C.-C., Lin, C.-Y., Akhauri, Y., Lin, W.-C., Wu, K.-C., Ceze, L., & Abdelfattah, M. S. (2025). xKV: Cross-Layer SVD for KV-Cache Compression. arXiv preprint arXiv:2503.18893. https://arxiv.org/abs/2503.18893 https://arxiv.org/html/2503.18893v1 https://x.com/gm8xx8/status/1904396074040656158 https://paperreading.club/page?id=294737 https://x.com/gm8xx8/status/1904396076427227409 https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management https://aclanthology.org/2025.coling-main.596/ https://chatpaper.com/chatpaper/zh-CN?id=3&date=1742832000&page=1 https://openreview.net/forum?id=z3JZzu9EA3 https://www.xueshuxiangzi.com/downloads/2025_3_25/2503.18893.pdf ```