BATQuant: Outlier-resilient MXFP4 Quantization via Learnable Block-wise Optimization
Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern accelerator architectures. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4. Recent studies attribute this failure to a fundamental format mismatch: global orthogonal rotations inadvertently transfer outlier energy across quantization blocks, inducing new outliers that disrupt local block-wise scaling, while often creating bimodal activation distributions that underutilize ...
| Authors | Ji-Fu Li, Manyi Zhang, Xiaobo Xia et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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