Google's new algorithm just shrunk 31GB of vectors into 4GB. Storing embeddings for RAG eats memory fast. Ten million documents in float32 takes 31 GB of RAM. A new open-source Rust vector index changes that math. TurboVec fits the same corpus into 4 GB. It runs on
Google TurboVec: Shrink Vector Embeddings from 31GB to 4GB
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