KVzap: Fast, Adaptive, and Faithful KV Cache Pruning

• URL: http://arxiv.org/abs/2601.07891v1
• Date: Mon, 12 Jan 2026 08:27:47 GMT
• Title: KVzap: Fast, Adaptive, and Faithful KV Cache Pruning
• Authors: Simon Jegou, Maximilian Jeblick,
• Abstract summary: We introduce KVzap, a fast, input-adaptive approximation of KVzip that works in both prefilling and decoding.<n> KVzap achieves $2$--$4times$ KV cache compression with negligible accuracy loss and achieves state-of-the-art performance on the KVpress leaderboard.
• Score: 1.3320917259299652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Growing context lengths in transformer-based language models have made the key-value (KV) cache a critical inference bottleneck. While many KV cache pruning methods have been proposed, they have not yet been adopted in major inference engines due to speed--accuracy trade-offs. We introduce KVzap, a fast, input-adaptive approximation of KVzip that works in both prefilling and decoding. On Qwen3-8B, Llama-3.1-8B-Instruct, and Qwen3-32B across long-context and reasoning tasks, KVzap achieves $2$--$4\times$ KV cache compression with negligible accuracy loss and achieves state-of-the-art performance on the KVpress leaderboard. Code and models are available at https://github.com/NVIDIA/kvpress.

Related papers

• Learning What to Write: Write-Gated KV for Efficient Long-Context Inference [10.915483460983411]
  We formalize KV cache management as a causal system of three primitives: KV Admission, Selection, and Eviction.<n>We instantiate KV Admission via Write-Gated KV, a lightweight mechanism that learns to predict token utility before it enters the cache.
  arXiv  Detail & Related papers  (2025-12-19T11:08:58Z)
• KV Cache Transform Coding for Compact Storage in LLM Inference [2.20003167536462]
  We present KVTC, a lightweight transform coder that compresses KV caches for compact on- GPU and off- GPU storage.<n>By exploiting redundancies in KV caches, KVTC achieves up to 20$times$ compression while maintaining reasoning and long-context accuracy.<n>We test KVTC with Llama 3, Mistral NeMo, and R1-Qwen 2.5 models across benchmarks including AIME25, LiveCodeBench, GSM8K, MMLU, Qasper, RULER, and MATH-500.
  arXiv  Detail & Related papers  (2025-11-03T18:20:35Z)
• R-KV: Redundancy-aware KV Cache Compression for Reasoning Models [77.84539432982307]
  We propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV)<n>R-KV preserves nearly 100% of the full KV cache performance using only 10% of the KV cache.<n>Remarkably, R-KV even achieves 105% of full KV cache performance with 16% of the KV cache.
  arXiv  Detail & Related papers  (2025-05-30T02:03:24Z)
• KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction [37.97434606840326]
  Transformer-based large language models (LLMs) cache context as key-value ( KV) pairs during inference.<n>As context length grows, KV cache sizes expand, leading to substantial memory overhead and increased attention latency.<n>This paper introduces KVzip, a query-agnostic KV cache eviction method enabling effective reuse of compressed KV caches across diverse queries.
  arXiv  Detail & Related papers  (2025-05-29T13:05:47Z)
• 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)
• RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression [25.190765258589707]
  We present RocketKV, a training-free KV cache compression strategy containing two consecutive stages.<n>In the first stage, it performs coarse-grain permanent KV cache eviction on the input sequence tokens.<n>In the second stage, it adopts a hybrid sparse attention method to conduct fine-grain top-k sparse attention.
  arXiv  Detail & Related papers  (2025-02-19T19:12:46Z)
• More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression [71.42818367729573]
  In large language models (LLMs), the memory usage of KV cache has become a critical bottleneck during inference.<n>The mainstream KV compression methods, including KV pruning and KV quantization, primarily focus on either token or precision dimension separately.<n>In this paper, we comprehensively investigate the token-precision trade-off in KV cache compression.
  arXiv  Detail & Related papers  (2024-12-17T09:20:31Z)
• 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)
• DiffKV: Differentiated Memory Management for Large Language Models with Parallel KV Compaction [33.936381781692994]
  DiffKV is a novel framework for efficient KV cache compression.<n>It exploits three levels of differentiation in the KV cache.<n>It is able to compress the KV cache by $2.7times$ to $5.7times$ with near-lossless accuracy.
  arXiv  Detail & Related papers  (2024-12-04T08:51:23Z)
• 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)
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache [67.9776980972508]
  We develop a tuning-free 2bit KV cache quantization algorithm named KIVI. KIVI can enable Llama, Falcon, and Mistral models to maintain almost the same quality while using $mathbf2.6times$ less peak memory.
  arXiv  Detail & Related papers  (2024-02-05T06:06:47Z)
• Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs [82.08922896531618]
  We introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs) We conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens.
  arXiv  Detail & Related papers  (2023-10-03T05:17:08Z)

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.