MODEL PROFILE

MiniMax-M3-MXFP4

amd · MiniMax-M3-MXFP4

- **Model Architecture:** MiniMaxM3SparseForConditionalGeneration - **Input:** Text, Image - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm**: 7.1.1 - **PyTorch**: 2.10.0 - **Transformers**: 5.2.0 - **Operating System(s):** Linux - **Inference Engine:** vLLM - **Model Optimizer:** AMD-Quark - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic The model was quantized from MiniMaxAI/MiniMax-M3 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4. The GSM8K results were obtained using the lm-eval framework, based on the Docker image `rocm/pytorch-private:vllm-hy-mm-06112026`. The vLLM shipped in that image was used as-is, with the patch from this PR (#45794) applied on top.

Visual QAmultimodallicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/31 02:47:10
OVERVIEW

Model overview

- **Model Architecture:** MiniMaxM3SparseForConditionalGeneration - **Input:** Text, Image - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm**: 7.1.1 - **PyTorch**: 2.10.0 - **Transformers**: 5.2.0 - **Operating System(s):** Linux - **Inference Engine:** vLLM - **Model Optimizer:** AMD-Quark - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic The model was quantized from MiniMaxAI/MiniMax-M3 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4. The GSM8K results were obtained using the lm-eval framework, based on the Docker image `rocm/pytorch-private:vllm-hy-mm-06112026`. The vLLM shipped in that image was used as-is, with the patch from this PR (#45794) applied on top.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

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