Efficient LLM Inference with Kcache
This addresses a memory bottleneck for efficient LLM inference in industry applications, representing a novel method for a known bottleneck.
The paper tackles the memory overhead problem of KV Cache in LLM inference by proposing KCache, which eliminates the need for KV Cache and improves throughput by 40% while maintaining accuracy.
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.