5 embedding compression techniques explained: 10M vectors shrink from 62GB to 2GB
blaizedsouza · x · 2026-09-04
A practical RAG engineering thread explains five embedding compression techniques with hard numbers: 10 million 1536-dimensional embeddings take 62GB in float32, 15GB in int8, but only 2GB when packed as bits — and that's just the raw payload, excluding ANN index, metadata, and allocator overhead.
Compression works along two axes: how many dimensions you store and how many bits per dimension. The five techniques target different parts of that payload:
- PCA: a post-training transform that projects embeddings into a lower-variance-derived space; works with any model but must be applied consistently to indexed vectors and queries.
- MRL: changes the training objective so the model natively supports multiple vector resolutions.
- Binary quantization and the rest: the linked article shows how Perplexity, Azure, and HubSpot use binary quantization to make RAG up to 32x more memory efficient, with code walkthroughs.
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