ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction

Abstract

Large Language Models (LLMs) are widely used in today’s tasks of natural language processing. To support applications like multi-turn chats, document understanding, and content generation, models with long context lengths are growing in importance. However, managing long contexts brings substantial challenges due to the expansion of key-value cache (KV cache). Longer KV cache requires larger memory, limiting the batch-size and thus decreasing throughput. Also, computing attention over long KV cache incurs more memory access, hurting the end-to-end latency. Prior works find that it is sufficient to use only the recent and high-impact tokens for attention computation, allowing the eviction of less vital tokens to reduce memory footprint. Nonetheless, we observe a dynamic shift in token importance across different decoding steps. Tokens initially evicted might regain importance after certain decoding steps. To address this, we propose A RK V ALE , a page-based KV cache manager that can recognize and recall important tokens evicted before. We asynchronously copy the filled page into external memory (e.g., CPU memory) as backup and summarize/compress it into a much smaller digest by constructing the bounding-volume of the keys in the KV-page