ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models

• URL: http://arxiv.org/abs/2510.01290v1
• Date: Wed, 01 Oct 2025 04:09:02 GMT
• Title: ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models
• Authors: Akshat Ramachandran, Marina Neseem, Charbel Sakr, Rangharajan Venkatesan, Brucek Khailany, Tushar Krishna,
• Abstract summary: ThinKV is a thought-adaptive KV cache compression framework.<n>It applies a hybrid quantization-eviction strategy, assigning token precision by thought importance.<n>Experiments on DeepSeek-R1-Distill, GPT-OSS, and NVIDIA AceReason show that ThinKV achieves near-lossless accuracy with less than 5% of the original KV cache.
• Score: 13.284627477293322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (KV) cache, quickly overwhelming GPU memory. To address this challenge, we propose ThinKV, a thought-adaptive KV cache compression framework. ThinKV is based on the observation that attention sparsity reveals distinct thought types with varying importance within the CoT. It applies a hybrid quantization-eviction strategy, assigning token precision by thought importance and progressively evicting tokens from less critical thoughts as reasoning trajectories evolve. Furthermore, to implement ThinKV, we design a kernel that extends PagedAttention to enable efficient reuse of evicted tokens' memory slots, eliminating compaction overheads. Extensive experiments on DeepSeek-R1-Distill, GPT-OSS, and NVIDIA AceReason across mathematics and coding benchmarks show that ThinKV achieves near-lossless accuracy with less than 5% of the original KV cache, while improving performance with up to 5.8x higher inference throughput over state-of-the-art baselines.

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