Practical AI Solution: KIVI
Reducing Memory Usage for Large Language Models
Large language models (LLMs) are powerful but require substantial memory for efficiency. KIVI is a plug-and-play quantization algorithm designed to compress key-value (KV) caches in LLMs, reducing memory needs without fine-tuning. Tests show it 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 simple and effective solution to the memory bottleneck problem. By compressing stored information, it enables LLMs to run faster, handle larger data batches, and boost overall performance.
If you want to evolve your company with AI and stay competitive, consider leveraging KIVI to redefine your work processes. To learn more about KIVI, read the Paper and visit the Github.
For further AI insights and practical solutions, connect with us at hello@itinai.com and stay informed on our Telegram t.me/itinainews or Twitter @itinaicom.
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