Qwen3-VL-30B-A3B-Thinking-AWQ (ROCm / RDNA3)

This model is a quantized version of Qwen/Qwen3-VL-30B-A3B-Thinking, specifically optimized for AMD GPUs (RDNA3 architecture) using ROCm and vLLM.

🔧 Quantization Details

The model was quantized using AWQ (Activation-aware Weight Quantization) with the following configuration, targeting efficient inference on AMD hardware:

• Method: AWQ
• Precision: W4A16 (4-bit weights, 16-bit activations)
• Group Size: 128
• Format: pack-quantized (compatible with vLLM compressed-tensors)
• Target Hardware: AMD RDNA3 (Tested on Radeon RX 7900 XTX)
• Minimum vLLM Version: 0.11+

Configuration Summary

{
  "quantization_config": {
    "quant_method": "compressed-tensors",
    "format": "pack-quantized",
    "config_groups": {
      "group_0": {
        "weights": {
          "num_bits": 4,
          "group_size": 128,
          "symmetric": true,
          "strategy": "group",
          "observer": "mse"
        },
        "targets": ["Linear"]
      }
    },
    "quantization_status": "compressed"
  },
  "dtype": "bfloat16",
  "model_type": "qwen3_vl_moe"
}

🚀 Deployment with vLLM (ROCm)

This quantized model is prepared to run on AMD RDNA3 GPUs using the ROCm backend of vLLM.

Recommended Environment

• Docker Image: docker.io/rocm/vllm
• GPU: AMD RDNA3 (e.g., 7900 XTX)
• Drivers: ROCm 6.x+

Run Command

The following command assumes a 2-GPU setup (e.g., 2× RX 7900 XTX) with Tensor Parallelism set to 2. The model fits on a single GPU; a second GPU was used solely to extend the context length.

python -m vllm.entrypoints.openai.api_server \
  --model navispace/Qwen3-VL-32B-Thinking-AWQ \
  --served-model-name Qwen3-VL-32B-Thinking-AWQ \
  --tensor-parallel-size 2 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --enable-chunked-prefill \
  --host 0.0.0.0 \
  --port 8000 \
  --enable-prefix-caching \
  --max-num-seqs 2 \
  --max-num-batched-tokens 8192 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --reasoning-parser deepseek_r1 \
  --trust-remote-code \
  --dtype float16 \
  --quantization compressed-tensors

This AWQ model preserves the original Qwen3-VL-30B-A3B-Thinking architecture and capabilities while significantly reducing VRAM requirements for ROCm-based deployments.

---

license: apache-2.0 pipeline_tag: image-text-to-text library_name: transformers

Qwen3-VL-30B-A3B-Thinking

Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.

This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.

Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.

Key Enhancements:

• Visual Agent: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.

• Visual Coding Boost: Generates Draw.io/HTML/CSS/JS from images/videos.

• Advanced Spatial Perception: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.

• Long Context & Video Understanding: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.

• Enhanced Multimodal Reasoning: Excels in STEM/Math—causal analysis and logical, evidence-based answers.

• Upgraded Visual Recognition: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.

• Expanded OCR: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.

• Text Understanding on par with pure LLMs: Seamless text–vision fusion for lossless, unified comprehension.

Model Architecture Updates:

1. Interleaved-MRoPE: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.

2. DeepStack: Fuses multi‑level ViT features to capture fine‑grained details and sharpen image–text alignment.

3. Text–Timestamp Alignment: Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.

This is the weight repository for Qwen3-VL-30B-A3B-Thinking.

---

Model Performance

Multimodal performance

Pure text performance

Quickstart

Below, we provide simple examples to show how to use Qwen3-VL with 🤖 ModelScope and 🤗 Transformers.

The code of Qwen3-VL has been in the latest Hugging face transformers and we advise you to build from source with command:

pip install git+https://github.com/huggingface/transformers
# pip install transformers==4.57.0 # currently, V4.57.0 is not released

Using 🤗 Transformers to Chat

Here we show a code snippet to show you how to use the chat model with transformers:

from transformers import Qwen3VLMoeForConditionalGeneration, AutoProcessor

# default: Load the model on the available device(s)
model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-30B-A3B-Thinking", dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen3-VL-30B-A3B-Thinking",
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Thinking")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

@article{Qwen2.5-VL,
  title={Qwen2.5-VL Technical Report},
  author={Bai, Shuai and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Song, Sibo and Dang, Kai and Wang, Peng and Wang, Shijie and Tang, Jun and Zhong, Humen and Zhu, Yuanzhi and Yang, Mingkun and Li, Zhaohai and Wan, Jianqiang and Wang, Pengfei and Ding, Wei and Fu, Zheren and Xu, Yiheng and Ye, Jiabo and Zhang, Xi and Xie, Tianbao and Cheng, Zesen and Zhang, Hang and Yang, Zhibo and Xu, Haiyang and Lin, Junyang},
  journal={arXiv preprint arXiv:2502.13923},
  year={2025}
}

@article{Qwen2VL,
  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
  author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
  journal={arXiv preprint arXiv:2409.12191},
  year={2024}
}

@article{Qwen-VL,
  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
  journal={arXiv preprint arXiv:2308.12966},
  year={2023}
}