MiniMax-M3 is a native multimodal MoE model. The original model card describes it as a ~428B parameter model with ~23B activated parameters and 1M context support.

License

This quantized checkpoint follows the license terms of the base model, MiniMaxAI/MiniMax-M3. The Hugging Face model-card metadata uses license: other because the MiniMax community license is not one of the Hub's enumerated license identifiers.

Model Optimizations

This checkpoint uses FP8 dynamic quantization to reduce memory and disk requirements while preserving model quality. Validation below compares this quantized checkpoint against the BF16 MiniMaxAI/MiniMax-M3 baseline.

Evaluation

The model was evaluated against BF16 MiniMaxAI/MiniMax-M3. Scores are averaged across seeds.

Benchmark	MiniMaxAI/MiniMax-M3	EmbeddedLLM/MiniMax-M3-FP8-dynamic	Recovery (%)
GSM8k Platinum	95.81	95.92	100.12
IfEval	80.65	79.42	98.47
AIME 2025	20.83	19.17	92.00
GPQA diamond	77.78	77.95	100.22
Math 500	81.20	79.93	98.44
Lcb Codegeneration V6	37.14	35.62	95.90
MMLU Pro Chat	79.85	79.62	99.72

Evaluation Setup

Standard seeds: 42, 1234, 4158
AIME 2025 seeds: 42, 1234, 4158, 5322, 1356, 9843, 3344, 5678
GSM8K Platinum cap: max_gen_toks=64000
IFEval, AIME, GPQA, Math 500, MMLU Pro Chat cap: max_gen_toks=4096
LiveCodeBench v6 cap: max_gen_toks=2048
MiniMax thinking mode: disabled
Runners: lm-eval harness and lighteval through LiteLLM endpoint mode

EmbeddedLLM/MiniMax-M3-FP8-dynamic

MiniMax-M3-FP8-dynamic

Model Overview

License

Model Optimizations

Evaluation

Evaluation Setup

EmbeddedLLM/
MiniMax-M3-FP8-dynamic