Quantization techniques are methods used to reduce the memory or computational cost of representing data by converting values from a high-precision format (like 32-bit floating points) to a lower-precision format (like 8-bit integers). In the context of vector compression, this means shrinking the size of vectors—such as embeddings or feature representations—by approximating their components with fewer bits. For example, a vector stored as 32-bit floats can be compressed into 8-bit integers, cutting its storage requirements by 75%. This process introduces some loss of precision, but when done carefully, the trade-off between accuracy and efficiency is manageable for many practical applications.
Quantization works by mapping ranges of continuous values into discrete buckets. A simple approach is scalar quantization, where each element in a vector is individually scaled and rounded to fit into a lower-bit representation. For instance, if a vector’s values range between -10 and 10, you might divide this range into 256 intervals (for 8-bit storage) and replace each original value with the nearest bucket’s midpoint. More advanced methods, like product quantization, split the vector into subvectors and quantize each separately using codebooks. For example, a 128-dimensional vector could be divided into 8 subvectors of 16 dimensions each. Each subvector is then replaced by the closest entry in a precomputed codebook (e.g., 256 entries per codebook, stored as 8-bit indices). This reduces storage further, as each subvector is represented by a single codebook index rather than individual values.
The primary benefit of quantization is efficient storage and faster computation. Compressed vectors take up less memory, enabling larger datasets to fit into RAM or GPU memory—critical for tasks like similarity search in recommendation systems. For example, a database of 1 million 512-dimensional vectors stored as 32-bit floats requires 2 GB of memory. Using 8-bit quantization cuts this to 0.5 GB, allowing more data to be processed in-memory. Additionally, operations like dot products or Euclidean distance calculations can be accelerated using integer arithmetic, which is faster on most hardware. However, developers must balance compression with accuracy: aggressive quantization (e.g., 4 bits) may degrade performance in downstream tasks. Testing with real data and validation metrics (like recall in nearest neighbor search) is essential to choose the right method. Libraries like FAISS or PQkNN provide built-in quantization tools, making it easier to experiment with these trade-offs.