Qwen3-VL is the latest generation of vision-language models in the Qwen series, delivering 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.

Quantization Parameters

Weight compression was performed using nncf.compress_weights with the following parameters:

mode: INT4_ASYM
ratio: 1.0
group_size: 128
awq: True
scale_estimation: False
sensitivity_metric: max_activation_variance
backup_mode: INT8_ASYM

For more information on quantization, check the OpenVINO model optimization guide.

Compatibility

The provided OpenVINO™ IR model is compatible with:

OpenVINO version 2026.3.0 and higher
Optimum Intel 1.27.0 and higher

Running Model Inference with Optimum Intel

Install packages required for using Optimum Intel integration with the OpenVINO backend:

pip install -U "git+https://github.com/huggingface/optimum-intel.git" torchvision "Pillow" --extra-index-url https://download.pytorch.org/whl/cpu
pip install --pre -U openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
pip install -U "transformers==5.2"

Run model inference:

python

import requests
from PIL import Image
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForVisualCausalLM

model_id = "OpenVINO/Qwen3-VL-4B-Instruct-int4-ov"
processor = AutoProcessor.from_pretrained(model_id)
model = OVModelForVisualCausalLM.from_pretrained(model_id)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=200)
print(processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

For more examples and possible optimizations, refer to the Inference with Optimum Intel.

Running Model Inference with OpenVINO GenAI

Install packages required for using OpenVINO GenAI.

pip install huggingface_hub "Pillow"
pip install --pre -U openvino openvino-tokenizers openvino-genai --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly

Download model from HuggingFace Hub

python

import huggingface_hub as hf_hub

model_id = "OpenVINO/Qwen3-VL-4B-Instruct-int4-ov"
model_path = "Qwen3-VL-4B-Instruct-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

Run model inference:

python

import numpy as np
import openvino as ov
import openvino_genai as ov_genai
import requests
from PIL import Image

device = "CPU"
pipe = ov_genai.VLMPipeline(model_path, device)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
image_tensor = ov.Tensor(np.array(image)[None])

print(pipe.generate("Describe this image.", image=image_tensor, max_new_tokens=200))

More GenAI usage examples can be found in OpenVINO GenAI library docs and samples

You can find more detailed usage examples in OpenVINO Notebooks:

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under Apache License Version 2.0 license. More details can be found in Qwen3-VL-4B-Instruct.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.