Zain Hasan, a former ML engineer at Weaviate and now a Senior AI/ML Engineer at Together, dives into the fascinating world of vector database quantization. He explains how quantization can drastically reduce storage costs, likening it to image compression. Zain discusses three quantization methods: binary, product, and scalar, each with unique trade-offs in precision and efficiency. He also addresses the speed and memory usage challenges of managing vector data, and hints at exciting future applications, including brain-computer interfaces.
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insights INSIGHT
Vector Storage Costs
Vectors, often 1000 numbers long, consume 32 bits each.
Millions of vectors in chatbots lead to exploding storage costs.
volunteer_activism ADVICE
Quantization for Vectors
Use quantization to compress vectors like JPEG compresses images.
This reduces storage needs while preserving most information.
insights INSIGHT
Quantization Trade-offs
Quantization methods, like image compression, trade accuracy for storage efficiency.
This trade-off is often negligible, like the quality loss in JPEGs.
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When you store vectors, each number takes up 32 bits.
With 1000 numbers per vector and millions of vectors, costs explode.
A simple chatbot can cost thousands per month just to store and search through vectors.
The Fix: Quantization
Think of it like image compression. JPEGs look almost as good as raw photos but take up far less space. Quantization does the same for vectors.
Today we are back continuing our series on search with Zain Hasan, a former ML engineer at Weaviate and now a Senior AI/ ML Engineer at Together. We talk about the different types of quantization, when to use them, how to use them, and their tradeoff.
Three Ways to Quantize:
Binary Quantization
Turn each number into just 0 or 1
Ask: "Is this dimension positive or negative?"
Works great for 1000+ dimensions
Cuts memory by 97%
Best for normally distributed data
Product Quantization
Split vector into chunks
Group similar chunks
Store cluster IDs instead of full numbers
Good when binary quantization fails
More complex but flexible
Scalar Quantization
Use 8 bits instead of 32
Simple middle ground
Keeps more precision than binary
Less savings than binary
Key Quotes:
"Vector databases are pretty much the commercialization and the productization of representation learning."
"I think quantization, it builds on the assumption that there is still noise in the embeddings. And if I'm looking, it's pretty similar as well to the thought of Matryoshka embeddings that I can reduce the dimensionality."
"Going from text to multimedia in vector databases is really simple."
"Vector databases allow you to take all the advances that are happening in machine learning and now just simply turn a switch and use them for your application."
How AI Is Built 