LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation

TL;DR

LookaheadKV introduces a lightweight, parameter-efficient framework for key-value cache eviction in large language models that predicts importance scores accurately without expensive draft generation, improving efficiency and speed.

Contribution

It presents a novel, low-overhead eviction method that enhances cache management in LLMs by accurately predicting importance scores without costly future response generation.

Findings

Outperforms recent baselines in long-context understanding tasks.

Reduces eviction cost by up to 14.5 times.

Achieves faster time-to-first-token in inference.

Abstract

Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves efficiency, the cache size grows linearly with the input sequence length, quickly becoming a bottleneck for long-context tasks. Existing solutions mitigate this problem by evicting prompt KV that are deemed unimportant, guided by estimated importance scores. Notably, a recent line of work proposes to improve eviction quality by "glimpsing into the future", in which a draft generator produces a surrogate future response approximating the target model's true response, and this surrogate is subsequently used to estimate the importance of cached KV more accurately. However, these approaches rely on computationally expensive draft generation, which introduces substantial prefilling overhead and limits their practicality in real-world deployment. To address this challenge, we propose LookaheadKV, a lightweight eviction framework that leverages the strength of surrogate future response without requiring explicit draft generation. LookaheadKV augments transformer layers with parameter-efficient modules trained to predict true importance scores with high accuracy. Our design ensures negligible runtime overhead comparable to existing inexpensive heuristics, while achieving accuracy superior to more costly approximation methods. Extensive experiments on long-context understanding benchmarks, across a wide range of models, demonstrate that our method not only outperforms recent competitive baselines in various long-context understanding tasks, but also reduces the eviction cost by up to 14.5x, leading to significantly faster time-to-first-token. Our code is available at https://github.com/SamsungLabs/LookaheadKV.