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Qdrant
RAG and search

Perplexity's New Late-Interaction Models Meet Qdrant's 21x Vector Compression

Our short summary of Qdrant’s engineering write-up. The full story, with all the detail, is on their blog.

Read the full write-up on qdrant.tech (opens in a new tab)

Problem

Storing millions of document image vectors required huge storage and memory.

Solution

Qdrant applied 1‑bit TurboQuant compression to reduce vector size 21×, keeping 96.5% retrieval quality and using RAM for compressed vectors.

96.5% quality retained

+ Suggest a write-up

Want the architecture, code and numbers? They’re in the original post.

Read the full write-up on qdrant.tech (opens in a new tab)

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