Computer Science > Hardware Architecture
arXiv:2603.08713 (cs)
[Submitted on 30 Jan 2026]
Title:Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
Authors:Jatin Chhugani, Geonhwa Jeong, Bor-Yiing Su, Yunjie Pan, Hanmei Yang, Aayush Ankit, Jiecao Yu, Summer Deng, Yunqing Chen, Nadathur Satish, Changkyu Kim
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Abstract: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).
| Subjects: | Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF) |
| Cite as: | arXiv:2603.08713 [cs.AR] |
| (or arXiv:2603.08713v1 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08713
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View a PDF of the paper titled Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction, by Jatin Chhugani and 10 other authors
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