KVComp: A High-Performance, LLM-Aware, Lossy Compression Framework for KV Cache

• URL: http://arxiv.org/abs/2509.00579v1
• Date: Sat, 30 Aug 2025 18:25:19 GMT
• Title: KVComp: A High-Performance, LLM-Aware, Lossy Compression Framework for KV Cache
• Authors: Bo Jiang, Taolue Yang, Youyuan Liu, Chengming Zhang, Xubin He, Sian Jin,
• Abstract summary: We present KVComp, a generic and efficient KV cache management framework optimized for long-text generation.<n> KVComp employs novel lossy compression techniques specifically designed for KV cache data characteristics.<n>We show that KVComp achieves on average 47% and up to 83% higher memory reduction rate compared to existing methods.
• Score: 7.019967158501771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based large language models (LLMs) demonstrate impressive potential in various practical applications. However, long context inference poses a significant challenge due to the enormous memory requirements of the key-value (KV) cache, which can scale to multiple gigabytes as sequence length and batch size increase. In this paper, we present KVComp, a generic and efficient KV cache management framework optimized for long-text generation that synergistically works with both latency-critical and throughput-critical inference systems. KVComp employs novel lossy compression techniques specifically designed for KV cache data characteristics, featuring careful co-design of compression algorithms and system architecture. Our approach maintains compatibility with the growing nature of KV cache while preserving high computational efficiency. Experimental results show that KVComp achieves on average 47\% and up to 83\% higher memory reduction rate compared to existing methods with little/no model accuracy degradation. Furthermore, KVComp achieves extremely high execution throughput, effectively reducing decompression overhead and, in some cases, even accelerating the matrix-vector multiplication operation and outperform cuBLAS-based attention kernels with less data movement.

Related papers

• DeltaKV: Residual-Based KV Cache Compression via Long-Range Similarity [50.52392445266824]
  We propose a residual-based KV cache compression framework motivated by long-range inter-token similarity and highly shared latent components in KV representations.<n>Instead of discarding tokens, DeltaKV encodes semantic residuals relative to retrieved historical references, preserving fidelity while substantially reducing storage.<n>Experiments show that DeltaKV reduces KV cache memory to 29% of the original while maintaining near-lossless accuracy on LongBench, SCBench, and AIME.
  arXiv  Detail & Related papers  (2026-02-08T15:14:36Z)
• PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compression [8.427136461713706]
  We present textbfPackKV, a generic and efficient KV cache management framework.<n>PackKV supports both latency-critical and throughput-critical inference scenarios.
  arXiv  Detail & Related papers  (2025-12-30T20:05:32Z)
• KV-CAR: KV Cache Compression using Autoencoders and KV Reuse in Large Language Models [3.5171501100868876]
  The KV cache grows with sequence length and embedding dimension, often exceeding the memory footprint of the model itself.<n>We present KV CAR, a unified and agnostic architecture framework that significantly reduces KV cache storage while maintaining model fidelity.<n> Evaluations on GPT 2 and TinyLLaMA models across Wikitext, C4, PIQA, and Winogrande datasets demonstrate that KV CAR achieves up to 47.85 percent KV cache memory reduction.
  arXiv  Detail & Related papers  (2025-12-07T08:40:52Z)
• XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression [54.28208936996186]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks.<n> Quantization has emerged as a promising solution to reduce memory consumption while preserving historical information.<n>We propose XQuant, a training-free and plug-and-play framework that achieves ultra-low equivalent bit-width KV cache quantization.
  arXiv  Detail & Related papers  (2025-10-13T10:17:21Z)
• CommonKV: Compressing KV Cache with Cross-layer Parameter Sharing [54.34080239841088]
  CommonKV is a training-free method for cross-layer KV cache compression through adjacent parameters sharing.<n>We show that the proposed method consistently outperforms existing low-rank and cross-layer approaches at various compression ratios.
  arXiv  Detail & Related papers  (2025-08-22T06:55:45Z)
• ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration [81.81027217759433]
  Large language models (LLMs) are often constrained by the excessive memory required to store the Key-Value ( KV) cache.<n>Recent methods have explored reducing the hidden dimensions of the KV cache, but many introduce additional computation through projection layers.<n>We propose ReCalKV, a post-training KV cache compression method that reduces the hidden dimensions of the KV cache.
  arXiv  Detail & Related papers  (2025-05-30T08:49:27Z)
• DBudgetKV: Dynamic Budget in KV Cache Compression for Ensuring Optimal Performance [125.81664663201282]
  We introduce a new KV cache compression method dubbed DBudgetKV.<n>It features an attention-based metric to signal when the remaining KV cache is unlikely to match the full-cache performance.<n>Our method achieves lossless KV pruning effectively and robustly, exceeding 25% compression ratio on average.
  arXiv  Detail & Related papers  (2025-02-24T06:33:39Z)
• ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression [10.003118268356017]
  Long context poses significant challenges for inference efficiency.<n>We introduce ClusterKV, which recalls tokens at the granularity of semantic clusters.<n>Experiment results show that ClusterKV attains negligible accuracy loss across various tasks with 32k context lengths.
  arXiv  Detail & Related papers  (2024-12-04T10:58:27Z)
• KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing [58.29726147780976]
  We propose a plug-and-play method called textit KVSharer, which shares the KV cache between layers to achieve layer-wise compression. Experiments show that textit KVSharer can reduce KV cache computation by 30%, thereby lowering memory consumption. We verify that textit KVSharer is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory.
  arXiv  Detail & Related papers  (2024-10-24T08:06:41Z)
• Lossless KV Cache Compression to 2% [22.98828332096935]
  This work introduces a novel architecture, Cross-Layer Latent Attention (CLLA), aimed at compressing the KV cache to less than 2% of its original size. CLLA integrates attention head/dimension reduction, layer sharing, and quantization techniques, into a cohesive framework.
  arXiv  Detail & Related papers  (2024-10-20T02:17:35Z)
• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)
• CSKV: Training-Efficient Channel Shrinking for KV Cache in Long-Context Scenarios [13.144156413032896]
  We introduce CSKV, a training-efficient Channel Shrinking technique for KV cache compression. We show that CSKV can reduce the memory overhead of the KV cache by 80% while maintaining the model's long-context capability. Our method can be seamlessly combined with quantization to further reduce the memory overhead, achieving a compression ratio of up to 95%.
  arXiv  Detail & Related papers  (2024-09-16T17:36:50Z)
• ThinK: Thinner Key Cache by Query-Driven Pruning [63.13363917871414]
  Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications.<n>This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference.<n>We propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels.
  arXiv  Detail & Related papers  (2024-07-30T17:59:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.