How to compute LLM embeddings 3X faster with model quantization

Nov 17, 2023 ·

LLM embedding is a method of representing words and phrases in a form that can be used by natural language processing (NLP) models. It involves creating a low-dimensional vector representation for each word or phrase which can then be used to compare texts and determine relationships between them. Model quantization allows this process to be sped up dramatically. This article explains the steps involved in model quantization and how it can help speed up the LLM embedding process.

The first step in model quantization is to reduce the number of parameters that are used in the model. This can be done by using smaller matrices, dropping certain features, or eliminating redundant features. These techniques help to reduce the number of calculations the model needs to make, thus speeding up its performance.

The second step is to use quantized versions of the existing parameters. This involves creating a smaller version of the original parameter matrix with fewer values. The main benefit of this is that it requires less memory to store, as well as allowing the model to run faster.

Finally, the third step is to apply the quantized parameters to the model. This requires running the model multiple times on different datasets to ensure that the new parameters are working correctly. After this has been tested, the model is ready to be deployed.

Overall, model quantization is an effective way to speed up LLM embedding. By reducing the number of parameters, using smaller matrices, and applying quantized versions of existing parameters, the process can be made much faster. This makes it especially useful for applications such as search engines and chatbots where speed is essential.