MF-QAT: Multi-Format Quantization-Aware Training for Elastic Inference
Quantization-aware training (QAT) is typically performed for a single target numeric format, while practical deployments often need to choose numerical precision at inference time based on hardware support or runtime constraints. We study multi-format QAT, where a single model is trained to be robust across multiple quantization formats. We find that multi-format QAT can match single-format QAT at each target precision, yielding one model that performs well overall across different formats, even formats that were not seen during training. To enable practical deployment, we propose the Slice-and-Scale conversion procedure for both MXINT and MXFP that converts a high-precision representatio...
| Authors | Zifei Xu, Sayeh Sharify, Hesham Mostafa |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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