  In the field of instruction-following large vision-language models (LVLMs),
the efficient deployment of these models faces challenges, notably due to the
high memory demands of their key-value (KV) caches. Conventional cache
management strategies for LLMs focus on cache eviction, which often fails to
address the specific needs of multimodal instruction-following models.
Recognizing this gap, in this paper, we introduce Elastic Cache, a novel
approach that benefits from applying distinct acceleration methods for
instruction encoding and output generation stages. We investigate the metrics
of importance in different stages and propose an importance-driven cache
merging strategy to prune redundancy caches. Instead of discarding less
important caches, our strategy identifies important key/value vectors as anchor
points. Surrounding less important caches are then merged with these anchors,
enhancing the preservation of contextual information in the KV caches while
yielding an arbitrary acceleration ratio. For instruction encoding, we utilize
the frequency to evaluate the importance of caches. Regarding output
generation, we prioritize tokens based on their distance with an offset, by
which both the initial and most recent tokens are retained. Results on a range
of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also
notably outperforms existing pruning methods in language generation across
various tasks. Code is available at https://github.com/liuzuyan/ElasticCache
