AnalysisAI ModelsOctober 19, 2026

FastH3-QAD-v2 trains 4-bit weights to close PTQ quality gap

Read original source →haoailab.com

Quantization-aware distillation of FastH3 V2 keeps the same memory footprint and file layout while matching or beating full-precision V2 with FP4 attention. Training used 3,000 text prompts, 300 steps on two GB200 nodes, about five hours.

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