Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing

• URL: http://arxiv.org/abs/2507.08045v1
• Date: Thu, 10 Jul 2025 01:51:17 GMT
• Title: Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing
• Authors: Junyi Wen, Junyuan Liang, Zicong Hong, Wuhui Chen, Zibin Zheng,
• Abstract summary: We present Krul, a multi-turn LLM inference system that enables accurate and efficient KV cache restoration.<n>Krul selects compression strategies based on attention similarity across layer pairs and uses a recomputation-loading pipeline to restore the KV cache.<n>It achieves a 1.5x-2.68x reduction in time-to-first-token (TTFT) and a 1.33x-2.35x reduction in KV cache storage.
• Score: 24.159793132911954
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficient state restoration in multi-turn conversations with large language models (LLMs) remains a critical challenge, primarily due to the overhead of recomputing or loading full key-value (KV) caches for all historical tokens. To address this, existing approaches compress KV caches across adjacent layers with highly similar attention patterns. However, these methods often apply a fixed compression scheme across all conversations, selecting the same layer pairs for compression without considering conversation-specific attention dynamics. This static strategy overlooks variability in attention pattern similarity across different conversations, which can lead to noticeable accuracy degradation. We present Krul, a multi-turn LLM inference system that enables accurate and efficient KV cache restoration. Krul dynamically selects compression strategies based on attention similarity across layer pairs and uses a recomputation-loading pipeline to restore the KV cache. It introduces three key innovations: 1) a preemptive compression strategy selector to preserve critical context for future conversation turns and selects a customized strategy for the conversation; 2) a token-wise heterogeneous attention similarity estimator to mitigate the attention similarity computation and storage overhead during model generation; 3) a bubble-free restoration scheduler to reduce potential bubbles brought by the imbalance of recomputing and loading stream due to compressed KV caches. Empirical evaluations on real-world tasks demonstrate that Krul achieves a 1.5x-2.68x reduction in time-to-first-token (TTFT) and a 1.33x-2.35x reduction in KV cache storage compared to state-of-the-art methods without compromising generation quality.

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