Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2603.08743 (cs)
[Submitted on 1 Mar 2026]
Title:Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention
Authors:Mengqi Liao, Lu Wang, Chaoyun Zhang, Bo Qiao, Si Qin, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Huaiyu Wan
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Abstract:With reasoning becoming the generative paradigm for large language models (LLMs), the memory bottleneck caused by KV cache during the decoding phase has become a critical factor limiting high-concurrency service. 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 LLM 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$\times$ speedup.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.08743 [cs.DC] |
| (or arXiv:2603.08743v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08743
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View a PDF of the paper titled Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention, by Mengqi Liao and 8 other authors
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