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

Giant language fashions (LLMs) are extremely helpful for duties like producing textual content or answering questions. Nevertheless, they face an enormous downside: they want a number of reminiscence to work effectively. This reminiscence shops details about phrases and phrases that the mannequin has seen earlier than. When the mannequin must generate new textual content, it appears up this saved data to assist it make choices. However the extra reminiscence the mannequin wants, the slower it runs, and generally, it may well even run out of reminiscence altogether.

One solution to scale back the quantity of reminiscence that LLMs want is to make use of quantization. Quantization is like compressing the knowledge in order that it takes up much less house. Some current options use quantization however typically require a number of fine-tuning to work properly. This fine-tuning course of could be time-consuming and sophisticated, making it troublesome for researchers and builders to make use of these options successfully.

Meet KIVI: a plug-and-play quantization algorithm particularly designed for key-value (KV) caches in LLMs. It really works by compressing the knowledge saved within the cache in order that it takes up much less house while not having any fine-tuning. Which means researchers and builders can use KIVI with out having to spend so much of time tweaking it to work with their particular LLM.

Checks have proven that KIVI is very efficient at lowering reminiscence utilization with out sacrificing efficiency. In reality, it may well scale back reminiscence utilization by as much as 2.6 instances in comparison with different quantization strategies. Which means LLMs utilizing KIVI can run sooner and deal with bigger batches of information, resulting in throughput enhancements of as much as 3.47 instances in real-world situations. For instance, when examined with Mistral-v0.2, KIVI maintained related accuracy to the full-precision baseline whereas utilizing 5.3 instances much less reminiscence for the KV cache.

In conclusion, KIVI provides a easy and efficient answer to the reminiscence bottleneck downside confronted by massive language fashions. KIVI reduces reminiscence utilization with out fine-tuning by compressing the knowledge saved in key-value caches. This enables LLMs to run sooner and deal with bigger batches of information, bettering general efficiency. Sooner or later, additional optimizations could also be made to cut back the overhead of the quantization course of, making KIVI much more environment friendly and simple to make use of.

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