amd / DeepSeek-V3.2-mxfp4

Model Overview

• Model Architecture: DeepSeek-V3.2
  • Input: Text
  • Output: Text
• Supported Hardware Microarchitecture: AMD MI350/MI355
• ROCm : 7.0
• PyTorch : 2.8.0
• Transformers : 4.53.0
• Operating System(s): Linux
• Inference Engine: SGLang / vLLM
• Model Optimizer: AMD-Quark (V0.10)
  • Weight quantization: OCP MXFP4, Static
  • Activation quantization: OCP MXFP4, Dynamic
• Calibration Dataset: Pile

This model was built with deepseek-ai DeepSeek-V3.2 model by applying AMD-Quark for MXFP4 quantization.

Model Quantization

The model was quantized from deepseek-ai/DeepSeek-V3.2 using AMD-Quark . Both weights and activations were quantized to MXFP4 format.

Deployment

This model can be deployed efficiently using the SGLang and vLLM backends.

Evaluation

export VLLM_USE_V1=1  
export SAFETENSORS_FAST_GPU=1
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_MOE=1
export VLLM_ROCM_USE_AITER_FP8BMM=0
export VLLM_ROCM_USE_AITER_FP4BMM=0
model_path="/shareddata/deepseek-ai/DeepSeek-V3.2-mxfp4"
vllm serve $model_path \
  --tensor-parallel-size 4 \
  --data-parallel-size 1 \
  --max-num-batched-tokens 32768 \
  --trust-remote-code \
  --no-enable-prefix-caching \
  --disable-log-requests \
  --kv-cache-dtype bfloat16 \
  --gpu_memory_utilization 0.85 \
  --compilation-config '{"cudagraph_mode": "FULL_AND_PIECEWISE"}' \
  --block-size 1

lm_eval \
  --model local-completions \
  --tasks gsm8k \
  --model_args model=/shareddata/deepseek-ai/DeepSeek-V3.2-mxfp4,base_url=http://127.0.0.1:8000/v1/completions \
  --batch_size auto \
 

Accuracy

Reproduction

docker: rocm/vllm-private:nightly-dpskv3.2-mxfp4

vllm main: 0900cedb3f89e475bea256c4cf5a13b5f02635bc

License

Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.