Product Quantization: Performance vs Memory Trade-offs

Analysis of 1536-dimensional vectors with overquery factor = 5. Point size represents recall quality.

Key Trade-offs

  • 16 subspaces: 384× compression, but 60% recall loss
  • 64 subspaces: 96× compression, 10% recall loss
  • 192 subspaces: 32× compression, full recall maintained

Performance Reality

  • • Aggressive compression (16 subspaces) doesn't improve query time
  • • Overquery factor of 5× needed to compensate for quality loss
  • • Best compression comes with 90% recall degradation
  • • Usable configurations (192 subspaces) still provide 32× compression

Insight: The "sweet spot" for Product Quantization isn't about maximum compression, but finding the balance between memory savings and acceptable recall. For production systems, 64-192 subspaces often provide the best trade-off between compression ratio and search quality.