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Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference

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Feng, Yuan, Lv, Junlin, Cao, Yukun, Xie, Xike, Zhou, S. Kevin·2025·Advances in Neural Information Processing Systems 38
DOI 10.52202/085713-3773
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Abstract

Large Language Models have excelled in various domains but face efficiency challenges due to the growing Key-Value (KV) cache required for long-sequence inference. Recent efforts aim to reduce KV cache size by evicting vast non-critical cache elements during runtime while preserving generation quality. However, these methods typically allocate compression budgets uniformly across all attention heads, ignoring the unique attention patterns of each head. In this paper, we establish a theoretical loss upper bound between pre- and post-eviction attention output, explaining the optimization target of prior cache eviction methods, while guiding the optimization of adaptive budget allocation. Base on this, we propose {\it Ada-KV}, the first head-wise adaptive budget allocation strategy. It offers plug-and-play benefits, enabling seamless integration with prior cache eviction methods. Extensive evaluations on 13 datasets from Ruler and 16 datasets from LongBench, all conducted under both question-aware and question-agnostic scenarios, demonstrate substantial quality improvements over existing methods. Our code is available at https://github.com/FFY0/AdaKV.

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Image 1
CC-BY · From the PDF
Figure 2 Adaptive allocation yields lower practical eviction losses (Llama3.1-8B-Instruct on Qasper Dataset).
Figure 2 Adaptive allocation yields lower practical eviction losses (Llama3.1-8B-Instruct on Qasper Dataset).CC-BY · From the PDF
Figure 3 Average Score on Ruler Among 13 Datasets.
Figure 3 Average Score on Ruler Among 13 Datasets.CC-BY · From the PDF
Figure 4 Subtask Analysis on Ruler (Question-agnostic, Llama-3.1-8B-Instruct).
Figure 4 Subtask Analysis on Ruler (Question-agnostic, Llama-3.1-8B-Instruct).CC-BY · From the PDF
Figure 5 Average Score on LongBench among 16 Datasets under Fixed Budgets.
Figure 5 Average Score on LongBench among 16 Datasets under Fixed Budgets.CC-BY · From the PDF
Image 6
CC-BY · From the PDF
Figure 7 M-QA Subtask Analysis on Ruler for Question-agnostic Scenario (Llama3.1-8B-Instruct)
Figure 7 M-QA Subtask Analysis on Ruler for Question-agnostic Scenario (Llama3.1-8B-Instruct)CC-BY · From the PDF
Figure 8 Subtask Analysis on Ruler (Question-aware, Llama3.1-8B-Instruct).
Figure 8 Subtask Analysis on Ruler (Question-aware, Llama3.1-8B-Instruct).CC-BY · From the PDF
Figure 9 Subtask Analysis on Ruler (Question-agnostic, Mistral-7B-Instruct-v0.2).
Figure 9 Subtask Analysis on Ruler (Question-agnostic, Mistral-7B-Instruct-v0.2).CC-BY · From the PDF
Figure 10 Subtask Analysis on Ruler (Question-aware, Mistral-7B-Instruct-v0.2).
Figure 10 Subtask Analysis on Ruler (Question-aware, Mistral-7B-Instruct-v0.2).CC-BY · From the PDF
Figure 11 Visualization of Heads' Concentrations
Figure 11 Visualization of Heads' ConcentrationsCC-BY · From the PDF

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Metadata

Authors
Feng, Y., Lv, J., Cao, Y., Xie, X., & Zhou, S. K.
Affiliations
  • University of Science and Technology of China (CN)
  • CAS
ORCID
Venue
Advances in Neural Information Processing Systems 38
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2025
Pages
125462-125498
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/085713-3773
OpenAlex
W4406071672
Cited by
5
References
3
Citations by year
2026: 4 · 2025: 1
FWCI
4.74
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
National Natural Science Foundation of China (62271465)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
KV cache eviction, large language model inference

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Feng, Y., Lv, J., Cao, Y., Xie, X., & Zhou, S. K. (2025). Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference. Advances in Neural Information Processing Systems 38. https://doi.org/10.52202/085713-3773