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A tweet thread introduces a visualizer for micro-scaling/block quant formats like NVFP4 and MXFP4, explaining how these low-precision floats work and their use in LLM inference to reduce memory bandwidth demands.
A technical comparison between nvfp4 and mxfp4 formats, highlighting that nvfp4 uses an additional tensor-wise scale factor to overcome fp4's range limit, allowing more precision in block-wise scale factors.
DealignAI releases CRACK-abliterated and MXFP4/MXFP8 quantized versions of Qwen3.6-27B and 35B models, preserving MTP for faster speculative decoding on Apple Silicon.
This paper decomposes MXFP4 quantization error into three additive components—scale bias, deadzone truncation, and grid noise—and proposes targeted corrections that recover BF16 accuracy to within 0.7 pp on Qwen2.5-3B and 3.0 pp on Qwen3-30B-A3B-Base for LLM reinforcement learning post-training.