Efficient LLM Inference with Kcache

• URL: http://arxiv.org/abs/2404.18057v1
• Date: Sun, 28 Apr 2024 03:11:42 GMT
• Title: Efficient LLM Inference with Kcache
• Authors: Qiaozhi He, Zhihua Wu,
• Abstract summary: Large Language Models (LLMs) have had a profound impact on AI applications. KV Cache technology is one of the most widely used techniques in the industry. We propose a novel KCache technique to alleviate the memory bottleneck issue during the LLMs inference process.
• Score: 3.945956673130761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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