Quantization risks of FP4 inference for small dense models
September 19, 2026
FP4 and MXFP4 inference engines significantly degrade performance in small dense models, leading to high hallucination rates and logical errors. While larger models may tolerate the precision loss, small models lack the redundancy to maintain functional accuracy under aggressive 4-bit quantization.
HOW THIS AFFECTS YOU
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builderAvoid using FP4 quantization for small models to prevent catastrophic logical failures in production.
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researcherInvestigate the precision floor for dense architectures under sub-8-bit quantization regimes.