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IVF_SQ8 Index
Converts 32-bit floats to 8-bit integers · 75% less memory, minimal accuracy loss
SQ8 maps each 32-bit float to an 8-bit integer (0–255) using the global min/max range. 4x less memory per value, similarity is approximate but very close.
Formula:
round( (v − min) ÷ (max − min) × 255 )
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Query Vector
Editable
Quantized →
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📏 Global Value Range
Min/max across ALL values, sets the 0–255 mapping scale.
Min: —
Max: —
💾 Memory Comparison
Original (32-bit floats × 3)12 bytes
SQ8 (8-bit ints × 3)3 bytes (75% saved!)
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Stored Vectors
Edit any float. 8-bit integers and scores update instantly.
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Approximate Ranked Results
LiveSimilarity computed using 8-bit quantized values.
💡 Key Idea
4 bytes → 1 byte per value. 75% memory saved, minimal accuracy loss.
✅ Best For
Large datasets needing 75% less RAM with near-original accuracy.
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Tips
3 tips
Scalar quantization shrinks memory. Converting 32-bit floats to 8-bit integers cuts storage by roughly 75%.
The accuracy loss is usually small, quantization introduces tiny rounding errors, but rankings stay close to the original.
Great option when you need to fit many more vectors in the same RAM and can tolerate a slight accuracy trade-off.
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Key Takeaway
IVF_SQ8 combines cluster search with 8-bit compression, cutting memory use by ~75% with only a minor accuracy loss.