@inproceedings{liao-etal-2026-zipage,
title = "Zipage: Maintain High Request Concurrency for {LLM} Reasoning through Compressed {P}aged{A}ttention",
author = "Liao, Mengqi and
Wang, Lu and
Zhang, Chaoyun and
Qiao, Bo and
Qin, Si and
Lin, Qingwei and
Rajmohan, Saravan and
Zhang, Dongmei and
Wan, Huaiyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.381/",
pages = "7716--7737",
ISBN = "979-8-89176-395-1",
abstract = "With reasoning becoming the generative paradigm for large language models, the memory bottleneck caused by KV cache during the inference phase has become a critical factor limiting high-concurrency service capabilities. Although existing KV cache eviction methods address the memory issue, most of them are impractical for industrial-grade applications. This paper introduces Compressed PagedAttention, a method that combines token-wise KV cache eviction with PagedAttention. We propose a comprehensive scheduling strategy and support prefix caching and asynchronous compression for Compressed PagedAttention. Based on this, we have developed a high-concurrency inference engine, Zipage. On large-scale mathematical reasoning tasks, Zipage achieves around 95{\%} of the performance of Full KV inference engines while delivering over 2.1 speedup."
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<abstract>With reasoning becoming the generative paradigm for large language models, the memory bottleneck caused by KV cache during the inference phase has become a critical factor limiting high-concurrency service capabilities. Although existing KV cache eviction methods address the memory issue, most of them are impractical for industrial-grade applications. This paper introduces Compressed PagedAttention, a method that combines token-wise KV cache eviction with PagedAttention. We propose a comprehensive scheduling strategy and support prefix caching and asynchronous compression for Compressed PagedAttention. Based on this, we have developed a high-concurrency inference engine, Zipage. On large-scale mathematical reasoning tasks, Zipage achieves around 95% of the performance of Full KV inference engines while delivering over 2.1 speedup.</abstract>
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%0 Conference Proceedings
%T Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention
%A Liao, Mengqi
%A Wang, Lu
%A Zhang, Chaoyun
%A Qiao, Bo
%A Qin, Si
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Zhang, Dongmei
%A Wan, Huaiyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liao-etal-2026-zipage
%X With reasoning becoming the generative paradigm for large language models, the memory bottleneck caused by KV cache during the inference phase has become a critical factor limiting high-concurrency service capabilities. Although existing KV cache eviction methods address the memory issue, most of them are impractical for industrial-grade applications. This paper introduces Compressed PagedAttention, a method that combines token-wise KV cache eviction with PagedAttention. We propose a comprehensive scheduling strategy and support prefix caching and asynchronous compression for Compressed PagedAttention. Based on this, we have developed a high-concurrency inference engine, Zipage. On large-scale mathematical reasoning tasks, Zipage achieves around 95% of the performance of Full KV inference engines while delivering over 2.1 speedup.
%U https://aclanthology.org/2026.findings-acl.381/
%P 7716-7737
Markdown (Informal)
[Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention](https://aclanthology.org/2026.findings-acl.381/) (Liao et al., Findings 2026)
ACL
- Mengqi Liao, Lu Wang, Chaoyun Zhang, Bo Qiao, Si Qin, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, and Huaiyu Wan. 2026. Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention. In Findings of the Association for Computational Linguistics: ACL 2026, pages 7716–7737, San Diego, California, United States. Association for Computational Linguistics.