AI BriefingOptimizing Search Speed and Cost with Embedding Quantization
Key point
Embedding quantization techniques are presented as a way to reduce the memory usage and cost of vector search while increasing speed.
Details
Embeddings represent text, images, and more as n-dimensional vectors for use in similarity search, but using high-dimensional float32 embeddings at scale results in enormous memory usage and cost. For example, processing 250 million vectors requires about 1TB of memory.
Two key quantization techniques are proposed to address this:
- Binary Quantization: Compresses each dimension to 1 bit, dramatically reducing memory usage.
- Scalar (int8) Quantization: Converts float32 to int8, reducing size while minimizing loss of precision.
Additionally, by combining a Rescoring technique—quickly extracting candidates using quantized vectors and then re-ranking them with the original vectors—it's possible to maximize performance while maintaining search accuracy. Experiments using 41 million Wikipedia data points demonstrated the efficiency of this approach in real-world search scenarios.
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