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

• URL: http://arxiv.org/abs/2603.08743v1
• Date: Sun, 01 Mar 2026 14:01:36 GMT
• Title: Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention
• Abstract summary: KV cache during the decoding phase has become a critical factor limiting high-concurrency service.<n>This paper introduces Compressed PagedAttention, a method that combines token-wise KV cache eviction with PagedAttention.<n>Based on this, we have developed a high-concurrency LLM inference engine, Zipage.
• Score: 58.012620801746046
• License: http://creativecommons.org/licenses/by/4.0/
• 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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