Binary Quantization: Make RAG 32x More Memory-Efficient
Learn how binary quantization makes RAG up to 32x more memory-efficient and enables querying 36M+ vectors in under 30 ms.
How to make RAG 32x more memory-efficient 😨
There’s a simple technique, widely used in the industry, that makes RAG about 32x more memory-efficient.
Perplexity uses it in its search index. Azure uses it in its search pipeline. HubSpot uses it in its AI assistant.
To understand it, here’s a guide where you’ll build a RAG system that queries 36M+ vectors in <30 ms.
And the technique that makes this possible is called binary quantization.
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