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arXiv · 2603.08743

Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention

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.

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Mengqi Liao, Lu Wang, Chaoyun Zhang, Bo Qiao, Si Qin, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Huaiyu Wan. 2026-03-01. Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention. https://arxiv.org/abs/2603.08743

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