Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

===== LTQUANT is a methods spec to show that quantized models (Q8/Q4) with LoreTokens + SAIQL can match FP16 accuracy while cutting tokens, latency, and cost. It: =====
* Sets non-inferiority bars (±1–2% accuracy vs FP16).
* Targets ≥50–70% token reduction and ≥2× cost-per-answer reduction on representative workloads. LTQUANT_SAIQL_Quantization_Pari…
* Defines how to run controlled experiments (fixed seeds, shared datasets, etc.) so vendors can prove parity claims.

Key idea: LoreTokens + SAIQL aren’t just compression; they’re how you keep quality while squeezing models and context.