The world of multimodal AI just received a major upgrade of Qwen2.5-VL, the most popular open-source vision model till now. Qwen3-VL, is the newest and most capable vision-language model in the Qwen family. Designed to understand, reason, and act across text, images, and video, Qwen3-VL isn’t just an upgrade, it’s a complete redefinition of what a visual-language model can do. With deeper visual reasoning, stronger spatial understanding, and massive context handling (up to 1M tokens), it seamlessly bridges perception and cognition. If you’re analyzing complex diagrams, interpreting entire books or hours-long videos, or even automating on-screen actions like a visual agent, Qwen3-VL performs with human-like depth and precision. Its Advanced Spatial Perception enables real-world reasoning and 3D grounding, while DeepStack and Interleaved-MRoPE architectures deliver unmatched fine-grained comprehension, making it ideal for STEM, creative design, and embodied AI applications. Paired with expanded OCR support for 32 languages and state-of-the-art text understanding on par with top pure-LLMs, this model represents the next frontier of unified multimodal intelligence.
In this guide, we’ll show you exactly how to install and run Qwen3-VL-30B-A3B-Instruct on your system, so you can experience firsthand how this model transforms visual understanding, reasoning, and automation into a single, powerful AI workflow.
Multimodal Performance
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x A100 or 1x H100
- Storage: 50 GB (preferable)
- VRAM: at least 80 GB
- Anaconda installed
Step-by-step process to install and run Qwen3-VL-30B-A3B-Instruct
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x H100 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n qwen python=3.11 -y && conda activate qwen
Output:
2. Install required dependencies.
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install git+https://github.com/huggingface/transformers
pip install git+https://github.com/huggingface/accelerate
pip install git+https://github.com/huggingface/diffusers
pip install huggingface_hub
pip install einops timm pillow
pip install sentencepiece bitsandbytes protobuf decord numpy
Output:
3. Install and run jupyter notebook.
conda install -c conda-forge --override-channels notebook -y
conda install -c conda-forge --override-channels ipywidgets -y
jupyter notebook --allow-root
4. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the jupyter notebook session on your local browser.
Run the following command in your local terminal after replacing:
<YOUR_SERVER_PORT> with the PORT allotted to your remote server (For the NodeShift server – you can find it in the deployed GPU details on the dashboard).
<PATH_TO_SSH_KEY> with the path to the location where your SSH key is stored.
<YOUR_SERVER_IP> with the IP address of your remote server.
ssh -L 8888:localhost:8888 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Output:
After this copy the URL you received in your remote server:
And paste this on your local browser to access the Jupyter Notebook session.
Step 8: Download and Run the model
- Open a Python notebook inside Jupyter.
2. Download the model checkpoints.
import torch
from transformers import Qwen3VLMoeForConditionalGeneration, AutoProcessor
# default: Load the model on the available device(s)
model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
"Qwen/Qwen3-VL-30B-A3B-Instruct", 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-Instruct",
# dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct")
Output:
3. Run the model for inference.
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"
)
inputs = {k: v.to(model.device) for k, v in inputs.items() if torch.is_tensor(v)}
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)
Input Image:
Generated Text:
Conclusion
Qwen3-VL is truly an intelligent multimodal model that not only see and describe but also reason, interpret, and act with precision across images, video, and text. From its groundbreaking Interleaved-MRoPE architecture and DeepStack fusion to its visual agent capabilities and 1M-token long-context reasoning, it brings together perception and cognition like never before. With NodeShift Cloud, running Qwen3-VL becomes effortless, no complex environment setup, driver issues, or dependency management. You get instant access to optimized GPU environments, preconfigured model execution pipelines, and interactive examples that let you focus on experimentation and innovation rather than infrastructure.
