---
base_model:
- MiniMaxAI/MiniMax-M3.1-Flash
license: other
license_name: minimax-community
license_link: LICENSE
---

# Model Overview

- **Model Architecture:** MiniMaxM3SparseForConditionalGeneration
  - **Input:** Text, Image, Video
  - **Output:** Text
- **Supported Hardware Microarchitecture:** AMD MI350/MI355
- **ROCm**: 7.2.3
- **PyTorch**: 2.13.0
- **Transformers**: 5.19.0
- **Operating System(s):** Linux
- **Inference Engine:** vLLM, SGLang
- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html)
  - **Routed-expert weight quantization:** OCP MXFP4, Static
  - **Routed-expert activation quantization:** OCP MXFP4, Dynamic
  - **All other layers**: unchanged precision compared to base [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash)

# Model serving

Please refer to [vLLM recipe](https://recipes.vllm.ai/) and [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M3.1).


Example usage:

## vLLM

```bash
VLLM_ROCM_USE_AITER=1 \
vllm serve amd/MiniMax-M3.1-Flash-Quark-MXFP4 \
    --tensor-parallel-size 4 \
    --reasoning-parser minimax_m3 \
    --block-size 128 \
    --speculative-config '{"method":"dspark","model":"amd/MiniMax-M3.1-Flash-Quark-MXFP4/dspark","num_speculative_tokens":7,"attention_backend":"FLASHINFER_TS","kv_cache_dtype":"fp8","draft_sample_method":"probabilistic","rejection_sample_method":"block"}' \
    --disable-hybrid-kv-cache-manager"
```

## SGLang

```bash
SGLANG_USE_AITER=1 \
sglang serve \
  --trust-remote-code \
  --model-path amd/MiniMax-M3.1-Flash-Quark-MXFP4 \
  --reasoning-parser minimax-m3 \
  --tool-call-parser minimax-m3 \
  --tp 4 \
  --speculative-algorithm DSPARK \
  --speculative-use-block-verification \
  --cuda-graph-backend-prefill breakable \
  --fp8-gemm-backend aiter \
  --chunked-prefill-size 32768 \
  --max-running-requests 128 \
  --cuda-graph-max-bs-decode 128 \
  --mem-fraction-static 0.8 \
  --tokenizer-worker-num 8 \
  --host 0.0.0.0 \
  --port 30000
```

# Model Quantization

The model's routed experts were quantized from NVFP4 to MXFP4 using [AMD-Quark](https://quark.docs.amd.com/latest/index.html).


**Quantization script:**

```bash
python quantize_quark.py \
  --model_dir MiniMaxAI/MiniMax-M3.1-Flash \
  --quant_scheme mxfp4 \
  --output_dir amd/MiniMax-M3.1-Flash-Quark-MXFP4 \
  --skip_evaluation --model_export hf_format --file2file_quantization \
  --exclude_layers \
    '*lm_head' \
    '*vision_tower*' \
    '*multi_modal_projector*' \
    '*patch_merge_mlp*' \
    '*self_attn*' \
    '*block_sparse_moe.gate' \
    'language_model.model.layers.[0-2].mlp.*' \
    '*block_sparse_moe.shared_experts.*' \
    '*dspark*' \
  --keep_excluded_layers_as_original_model_state
```

# Evaluation
The model was evaluated on GSM8K using vLLM and EvalScope.

### Accuracy

<table>
  <tr>
   <td><strong>Benchmark</strong></td>
   <td><strong>MiniMax/MiniMax-M3.1-Flash</strong></td>
   <td><strong>amd/MiniMax-M3.1-Flash-Quark-MXFP4 (this model)</strong></td>
   <td><strong>Recovery</strong></td>
  </tr>
  <tr>
   <td>GSM8K (EvalScope, 4-shot)</td>
   <td>97.2%</td>
   <td>96.9%</td>
   <td>99.69%</td>
  </tr>
</table>


### Reproduction

The GSM8K results were obtained using EvalScope and vLLM.

#### Launching server
```bash
VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MOE=1 HIP_VISIBLE_DEVICES=0,1,2,3 \
vllm serve amd/MiniMax-M3.1-Flash-Quark-MXFP4 \
  --tensor-parallel-size 4 \
  --host 0.0.0.0 \
  --port 8101 \
  --trust-remote-code \
  --gpu-memory-utilization 0.9 \
  --max-model-len 131072 \
  --max-num-seqs 80 \
  --max-num-batched-tokens 32768 \
  --kv-cache-dtype fp8 \
  --reasoning-parser minimax_m3 \
  --moe-backend aiter
```


#### Evaluating model in a new terminal
```bash
evalscope eval \
  --model amd/MiniMax-M3.1-Flash-Quark-MXFP4 \
  --api-url http://localhost:8101/v1/chat/completions \
  --api-key EMPTY \
  --datasets gsm8k \
  --dataset-hub huggingface \
  --dataset-args '{"gsm8k":{"dataset_id":"openai/gsm8k"}}' \
  --dataset-dir hf_hub_cache \
  --eval-batch-size 32 \
  --generation-config '{"temperature":1,"top_p":1,"max_tokens":8192,"timeout":60000,"extra_body":{"chat_template_kwargs":{"thinking_mode":"enabled"}}}' \
  --work-dir logs/gsm8k
```

# Others

## Experimental MXFP4 shared experts

MXFP4 [online](https://docs.vllm.ai/en/stable/features/quantization/online/) shared experts quantization and shared experts fusion can optionally be enabled:

```bash
VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1 vllm serve \
    amd/MiniMax-M3.1-Flash-Quark-MXFP4 \
    ... \
    --quantization-config.targets '{"*shared_expert*": "mxfp4"}'
```

but is experimental and not the recommended default setup.

# License
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.