KIVI: A Plug-and-Play 2-bit KV Cache Quantization Algorithm without the Need for Any Tuning

Large language models (LLMs) are incredibly useful for tasks like generating text or answering questions. However, they face a big problem: they need a lot of memory to work efficiently. This memory stores information about words and phrases that the model has seen before. When the model needs to generate new text, it looks up this stored information to help it make decisions. But the more memory the model needs, the slower it runs, and sometimes, it can even run out of memory altogether.

One way to reduce the amount of memory that LLMs need is to use quantization. Quantization is like compressing the information so that it takes up less space. Some existing solutions use quantization but often require a lot of fine-tuning to work well. This fine-tuning process can be time-consuming and complicated, making it difficult for researchers and developers to use these solutions effectively.

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Meet KIVI: a plug-and-play quantization algorithm specifically designed for key-value (KV) caches in LLMs. It works by compressing the information stored in the cache so that it takes up less space without needing any fine-tuning. This means that researchers and developers can use KIVI without having to spend a lot of time tweaking it to work with their specific LLM.

Tests have shown that KIVI is highly effective at reducing memory usage without sacrificing performance. In fact, it can reduce memory usage by up to 2.6 times compared to other quantization methods. This means that LLMs using KIVI can run faster and handle larger batches of data, leading to throughput improvements of up to 3.47 times in real-world scenarios. For example, when tested with Mistral-v0.2, KIVI maintained similar accuracy to the full-precision baseline while using 5.3 times less memory for the KV cache.

In conclusion, KIVI offers a simple and effective solution to the memory bottleneck problem faced by large language models. KIVI reduces memory usage without fine-tuning by compressing the information stored in key-value caches. This allows LLMs to run faster and handle larger batches of data, improving overall performance. In the future, further optimizations may be made to reduce the overhead of the quantization process, making KIVI even more efficient and easy to use.


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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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