amd / Qwen3-Coder-480B-A35B-Instruct-MXFP4

Model Overview

• Model Architecture: Qwen3MoeForCausalLM
  • Input: Text
  • Output: Text
• Supported Hardware Microarchitecture: AMD MI300 MI350/MI355
• ROCm : 7.0
• PyTorch : 2.8.0
• Transformers : 4.57.6
• Operating System(s): Linux
• Inference Engine: SGLang / vLLM
• Model Optimizer: AMD-Quark (v0.11)
  • Weight quantization: OCP MXFP4, Static
  • Activation quantization: OCP MXFP4, Dynamic

Model Quantization

The model was quantized from Qwen/Qwen3-Coder-480B-A35B-Instruct using AMD-Quark . The weights are quantized to MXFP4 and activations are quantized to MXFP4.

Quantization scripts:

cd Quark/examples/torch/language_modeling/llm_ptq/
export exclude_layers="lm_head *self_attn* *mlp.gate"
python3 quantize_quark.py --model_dir $MODEL_DIR \
                          --quant_scheme mxfp4 \
                          --num_calib_data 128 \
                          --exclude_layers $exclude_layers \
                          --skip_evaluation \
                          --multi_gpu  \
                          --trust_remote_code \
                          --model_export hf_format \
                          --output_dir $output_dir

For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.

Evaluation

The model was evaluated on gsm8k benchmarks using the vllm framework.

Accuracy

Reproduction

The GSM8K results were obtained using the vLLM framework, based on the Docker image rocm/vllm:rocm7.0.0_vllm_0.11.2_20251210 , and vLLM is installed from source inside the container.

Preparation in container

# Reinstall vLLM
pip uninstall vllm -y
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.13.0
pip install -r requirements/rocm.txt
python setup.py develop
cd ..

Launching server

VLLM_ROCM_USE_AITER=1 \
VLLM_DISABLE_COMPILE_CACHE=1 \
vllm serve "$MODEL" \
    --tensor-parallel-size 4 \
    --trust-remote-code \
    --max-model-len 32768 \
    --port 8899  

Evaluating model in a new terminal

python vllm/tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port 8899 --num-questions 1000 --save-results logs

License

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