Vector Quantization

Vector quantization is a compression technique that reduces the precision of embedding vectors (from float32 to int8, for example), delivering significant memory savings and faster operations with minimal quality impact.

In vector databases like Qdrant, quantization becomes essential for millions of vectors where RAM is constrained. One million 768-dimensional vectors in float32 consume roughly 3 GB of memory—quantized to int8, they require only 0.75 GB.

The technique becomes most valuable at scale. While smaller SEO projects with thousands of URLs may not need quantization, it becomes essential for large-scale applications like e-commerce sites with hundreds of thousands of products, where memory efficiency is vital. The precision loss rarely affects practical tasks like duplicate detection or content clustering.