Show HN: TurboQuant for vector search – 2-4 bit compression (github.com) AI

Show HN spotlights py-turboquant (turbovec), an unofficial implementation of Google’s TurboQuant vector-search method that compresses high-dimensional embeddings to 2–4 bits per coordinate using a data-oblivious random rotation and math-derived Lloyd-Max quantization. The project is implemented in Rust with Python bindings via PyO3 and emphasizes zero training and fast indexing. Benchmarks on Apple Silicon and x86 compare favorably to FAISS (especially at 4-bit) in speed while achieving comparable or better recall, with much smaller index sizes than FP32.