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Quantization

How do we compress embeddings without losing meaning? From scalar quantization to Google's TurboQuant (ICLR 2026) — the math ContextCrunch uses to crunch your context.

TurboQuant — Google Research · ICLR 2026 Google's algorithm achieves 6× memory reduction on vector embeddings with near-zero accuracy loss. ContextCrunch applies this to your conversation's sentence embeddings.

Quantization levels — storage comparison d=384 embedding

32Float32 baseline

1,536 bytes/vec

8Int8 scalar

384 bytes · 4× smaller

4Product quant

192 bytes · 8× smaller

3.5TurboQuant

168 bytes · 9× smaller

Zero accuracy loss at 3.5 bits — TurboQuant's PolarQuant rotation makes the data distribution predictable, allowing near-optimal quantization. The 1-bit QJL stage eliminates residual bias.

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