# Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction

arXiv: https://arxiv.org/abs/2603.08713 · HTML: https://mlsanity.com/paper/2603.08713/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2026.

## Built on by (most cited first, 6 of 6)

- [SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization](https://mlsanity.com/paper/2605.12245/) (2026)
- [Quantizing Recursive Reasoning Models](https://mlsanity.com/paper/2607.16237/) (2026)
- [MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention](https://mlsanity.com/paper/2607.24377/) (2026)
- [FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling](https://mlsanity.com/paper/2608.01847/) (2026)
- [Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor](https://mlsanity.com/paper/2605.20402/) (2026)
- [Finer is Better (with the Right Scaling)](https://mlsanity.com/paper/2605.08565/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have intensified the need for low-precision formats that enable efficient, large-scale inference. The Open Compute Project (OCP) Microscaling (MX) standard is attractive due to its favorable hardware efficiency, but its 4-bit variant (MXFP4) lags behind NVIDIA's NVFP4 in accuracy, limiting adoption. We introduce two software-only techniques, Overflow-Aware Scaling (OAS) and Macro Block Scaling (MBS), that improve MXFP4 quantization fidelity without requiring hardware changes. OAS reduces overall errors by increasing effective dynamic range under power-of-two block scaling, while MBS allocates higher-precision scaling at a coarser granularity to better preserve outliers. Across multiple LLMs and standard downstream benchmarks, OAS and MBS reduce the end-to-end accuracy gap between MXFP4 and NVFP4 from about 10% to below 1% on average, while incurring modest GEMM overhead (6.2% on average). These results re-establish MXFP4 as a practical alternative to NVFP4, enabling near-NVFP4 accuracy while retaining MX's hardware-efficiency advantages (e.g., 12% relative area savings in tensor cores).