Faster, Cheaper Retrieval with Embedding Quantization

Embeddings are a fundamental component of most modern AI stack. When working with large document repositories, the computational costs of storing and retrieving embeddings can quickly become prohibitive. Fortunately, there’s a solution: embedding quantization.

What is Embedding Quantization?

Embedding quantization is the process of compressing high-dimensional embedding vectors into a more compact representation such as binary. Instead of storing each number in a 32-bit float, each value is reduced to a single bit — 0 for negative numbers and 1 for positive numbers. This process reduces the storage and memory requirements by 32 times!

While quantization is a lossy compression technique, meaning some information is lost, the performance impact is surprisingly small. Experiments show quantized embeddings can achieve high 90%+ of the accuracy of the original embeddings. And with techniques like oversampling and re-ranking, you can get results very close to the uncompressed embeddings.

Benefits of Quantization

The primary benefits of embedding quantization are:

1. Reduced storage costs — By converting each element in the vector to a single bit (0 or 1), the storage requirement per element drops from 32 bits to 1 bit. For large datasets, this translates to major cost savings.