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@vintcessun: Compressing 10 million vectors from 31GB to 4GB, with search even faster than FAISS — sounds crazy, but Turbovec actually did it. The core is Google's TurboQuant data-independent quantization: no training, no parameter tuning, just add vectors and index. Handwritten NEON/AVX-512 implementations are genuinely 12-20% faster, supporting filtered search by ID, saving a ton of post-processing hassle. Rust under the hood + pip install, minimal maintenance cost.

X AI KOLs Timeline ↗ · 2d ago Cached

Turbovec, based on Google's TurboQuant algorithm, compresses 10 million vectors from 31GB to 4GB, with search speed 12-20% faster than FAISS, supports filtered search, and offers a Rust implementation with a Python package.

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