MiniMax M2.5 NVFP4 EU AI Act Compliance Profile

Updated 2/16/2026 - Improved calibration coverage.

Model Description

MiniMax-M2.5-NVFP4 is an NVFP4-quantized version of MiniMaxAI/MiniMax-M2.5, a 230B-parameter Mixture-of-Experts language model with 10B active parameters.

The original model weights were converted from the official FP8 checkpoint to BF16, then quantized to NVFP4 (4-bit with blockwise FP8 scales per 16 elements) using NVIDIA Model Optimizer.

What's quantized

Only the MoE expert MLP layers (gate, up, and down projections) are quantized to NVFP4. Attention layers are left in BF16. Since the expert weights constitute the vast majority of model parameters in an MoE architecture, this still yields significant memory savings.

Calibration uses natural top-k routing rather than forcing all experts to activate, so each expert's quantization scales reflect the token distributions it actually sees during inference. To compensate, calibration was run on a vastly larger number of samples than typical to ensure broad expert coverage through natural routing alone.

Calibration dataset

Samples were drawn from a diverse mix of publicly available datasets spanning code generation, function/tool calling, multi-turn reasoning, math, and multilingual (English + Chinese) instruction following. System prompts were randomly varied across samples. The dataset was designed to broadly exercise the model's capabilities and activate diverse token distributions across expert modules.

Quality

MMLU-Pro results (thanks to Lavd for providing these):

| Category | Correct | Total | Accuracy | |---|---:|---:|---:| | Math | 1279 | 1351 | 94.7% | | Biology | 675 | 717 | 94.1% | | Physics | 1188 | 1299 | 91.5% | | Chemistry | 1035 | 1132 | 91.4% | | Business | 715 | 789 | 90.6% | | Computer Science | 366 | 410 | 89.3% | | Economics | 748 | 844 | 88.6% | | Psychology | 674 | 798 | 84.5% | | Health | 686 | 818 | 83.9% |

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Tags

Base Model