Large Language Models (LLMs) have made remarkable progress in processing
extensive contexts, with the Key-Value (KV) cache playing a vital role in
enhancing their performance. However, the growth of the KV cache in response to
increasing input length poses challenges to memory and time efficiency. To
address this problem, this paper introduces SnapKV, an innovative and
fine-tuning-free approach that efficiently minimizes KV cache size while still
delivering comparable performance in real-world applications.
We discover that each attention head in the model consistently focuses on
specific prompt attention features during generation. Meanwhile, this robust
pattern can be obtained from an `observation' window located at the end of the
prompts. Drawing on this insight, SnapKV automatically compresses KV caches by
selecting clustered important KV positions for each attention head. Our
approach significantly reduces the growing computational overhead and memory
footprint when processing long input sequences. Specifically, SnapKV achieves a
consistent decoding speed with a 3.6x increase in generation speed and an 8.2x
enhancement in memory efficiency compared to baseline when processing inputs of
16K tokens. At the same time, it maintains comparable performance to baseline
models across 16 long sequence datasets. Moreover, SnapKV can process up to
380K context tokens on a single A100-80GB GPU using HuggingFace implementation
with minor changes, exhibiting only a negligible accuracy drop in the
Needle-in-a-Haystack test. Further comprehensive studies suggest SnapKV's
potential for practical applications.

SnapKV 是一种创新且无需微调的方法，它通过选择每个注意力头的重要键值位置来高效地减小键值存储缓存的大小，从而在处理长输入序列时显著降低了计算开销和内存占用，同时保持了与基准模型相当的性能。