Are you looking to optimize the efficiency of large language models (LLMs) while reducing memory usage? Meet KIVI, a plug-and-play quantization algorithm designed to compress key-value (KV) caches in LLMs, cutting memory needs without the need for fine-tuning. Tests have shown that KIVI can reduce memory usage by up to 2.6 times, leading to throughput improvements of up to 3.47 times in real-world scenarios.
KIVI offers a straightforward and effective solution to the memory bottleneck problem. By compressing stored information, it enables LLMs to run faster, handle larger data batches, and enhance overall performance.
If you’re ready to take your company to the next level with AI and maintain a competitive edge, consider leveraging KIVI to redefine your work processes. For more information about KIVI, read the Paper and visit the Github.
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