#mixed-precision quantization

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2 papers match

cs.LG2026

Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate

Joshua Hill

The paper demonstrates that for 4‑bit mixed‑precision quantization, the loss from quantizing a set of layers is largely additive across individual layers, and introduces a simple c…

#mixed-precision quantization#model compression#sensitivity analysis#coverage model

cs.LG2026

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats

Giuseppe Franco, Ian Colbert, Pablo Monteagudo-Lago +2

The paper presents dMX, a differentiable framework that learns per-layer floating‑point bit‑widths for large language models, enabling mixed‑precision quantization that balances ac…

#mixed-precision quantization#floating-point formats#large language models#model compression