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Use your OpenAI commitments on Baseten open models . Learn more Model performance What makes a high-quality NVFP4 quantization? NVFP4 makes models smaller and faster, but quantizing some layers can hurt quality. An intuitive look at how to choose which layers can run at 4 bits. Authors Chloe Florit Published October 9, 2026 Share Published October 9, 2026 TL;DR A high-quality NVFP4 quantization comes down to choosing which layers can run at 4 bits and which need more precision. This post covers three ways to assign a format to each layer: heuristic-based algorithms decide by architecture; isolated-layer sensitivity scoring measures one layer at a time, so its scores can't be added; and SaturationQuant builds saturation into each score, so they can be added across a mixed-precision recipe. Saturation means each layer adds less loss when others are quantized. Quantization makes a model smaller and faster by storing its weights and activations in a lower-precision format. NVFP4 represents each value in 4 bits. With such coarse rounding, some layers lose information the model relies on. Other layers handle 4 bits with almost no..."},"evidence_pages":[{"is_primary":false,"source_match":false,"url":"https://arxiv.org/pdf/2607.12266","final_url":"https://arxiv.org/pdf/2607.12266","title":"What Makes A High Quality Nvfp4 Quantization","http_status":200,"content_type":"application/pdf","capture_method":"exa","fetched_at":"2026-10-09T16:02:59.455+00:00","bytes":939697,"raw_path":"38b2928f588f93ba9e598661ff460895e1c68ba18ccee1411d778634d57c8e61.pdf","content_hash":"73d29419d18c3f65d51d09e588a063f64e3f13f4e54a5007a2faba7fef33276c","excerpt_chars":1200,"truncated":true,"excerpt":"Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate arXiv is now an independent nonprofit! Learn more× Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate Joshua Hill Baseten Labs Work done during an internship at Baseten Labs, Inc. Inc. David R. Cheriton School of Computer Science University of Waterloo Abstract Mixed-precision quantization must decide which parts of a model to keep at higher precision. A common premise, shared by sensitivity-based methods such as HAWQ and CoopQ, is that the loss from quantizing a set of layers can be reconstructed from per-layer or pairwise sensitivities measured in isolation. We test this premise at the 4-bit weight-and-activation precisions now being deployed, treating the change in loss $f(S)$ from quantizing a layer set $S$ as a set function on the Boolean cube and analyzing it through two classical changes of basis. This analysis yields two findings. First, across configurations drawn from the deployment distribution, 85–93% of the variance of $f$ is explained by per-layer effects alone. 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