Show HN TurboQuant for vector search 24 bit compression

Image for article Show HN TurboQuant for vector search  24 bit compression
News Source : Github.com

News Summary

  • Compresses high-dimensional vectors to 2-4 bits per coordinate with near-optimal distortion.
  • Data-oblivious (no training), zero indexing time.
  • Each vector is encoded independently in ~4ms at d=1536.
  • A 1536-dim vector goes from 6,144 bytes (FP32) to 384 bytes (2-bit) That's 16x compression.
  • Instead of decompressing every database vector, we rotate the query once into the same domain and score directly against the codebook values.
Python implementation of TurboQuant for vector search.Compresses highdimensional vectors to 24 bits per coordinate with nearoptimal distortion. Dataoblivious (no training), zero indexing time. [+5038 chars]

Must read Articles