  Large Language Models(LLMs) have had a profound impact on AI applications,
particularly in the domains of long-text comprehension and generation. KV Cache
technology is one of the most widely used techniques in the industry. It
ensures efficient sequence generation by caching previously computed KV states.
However, it also introduces significant memory overhead. We discovered that KV
Cache is not necessary and proposed a novel KCache technique to alleviate the
memory bottleneck issue during the LLMs inference process. KCache can be used
directly for inference without any training process, Our evaluations show that
KCache improves the throughput of popular LLMs by 40% with the baseline, while
keeping accuracy.
