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 run 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 for 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 completely.

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

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

Testing has shown that KIVI is very effective in 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, resulting in performance improvements of up to 3.47x 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 adjustments by compressing 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 quantification process, making KIVI even more efficient and easier to use.

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