As noted, most quantization techniques require calibration using representative data to determine optimal quantization grids for specific model-dataset combinations. TurboQuant operates data-obliviously: the algorithm functions from fundamental principles near theoretical information limits without prior data exposure. This enables inference-time deployment across models without quantized model training. No specialized training or fine-tuning needed to achieve optimal compression without accuracy trade-offs.
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