Qwen2.5-VL is a powerful vision-language model built to understand both what you say and what you show. Whether it’s reading a chart, explaining a meme, or analyzing a full document with images—this model can do it all. It comes in multiple sizes (from 3B to 72B) so you can choose what fits your hardware, whether you’re running it on a basic GPU or a high-end cluster. It’s fast, surprisingly smart, and feels like you’re talking to a model that actually sees what you’re talking about.
Qwen2.5-VL – GPU Requirements by Model Version
| Model Version | Model Size | Input Types | Context Length | Minimum GPU (for inference) | Recommended GPU Setup | vCPUs | RAM | Disk |
|---|
qwen2.5vl:3b | 3.2 GB | Text, Image | 125K | NVIDIA T4 / RTX 3060 8GB | RTX A4000 / 1× A10 24GB | 4+ | 8 GB | 16 GB+ |
qwen2.5vl:7b | 6.0 GB | Text, Image | 125K | RTX A4000 16GB / RTX 3090 | 1× A100 40GB / RTX A6000 | 8+ | 16 GB | 32 GB+ |
qwen2.5vl:32b | 21 GB | Text, Image | 125K | 2× A100 40GB (or 1× H100 80GB) | 2× H100 80GB SXM | 24+ | 64 GB | 100 GB+ |
qwen2.5vl:72b | 71 GB | Text, Image | 125K | 4× A100 80GB / 2× H100 80GB | 4× H100 SXM with tensor parallelism | 32+ | 128 GB | 160 GB+ |
qwen2.5vl:latest | 6.0 GB | Text, Image | 125K | Same as 7B | Same as 7B | 8+ | 16 GB | 32 GB+ |
College-level Reasoning
| Benchmark | Qwen2.5-VL 72B | Gemini-2 Flash | GPT-4o | Claude 3.5 | Qwen2-VL 72B | Other Best |
|---|
| MMMU | 70.2 | 70.7 | 70.3 | 70.4 | 64.5 | 70.1 |
| MMMU Pro | 51.1 | 57.0 | 54.5 | 54.7 | 46.2 | 52.7 |
Document & Diagram Reading
| Benchmark | Qwen2.5-VL 72B | Gemini-2 | GPT-4o | Claude 3.5 | Qwen2-VL | Other Best |
|---|
| DocVQA | 96.4 | 92.1 | 91.1 | 95.2 | 96.5 | 96.1 |
| InfoVQA | 87.3 | 77.8 | 80.7 | 74.3 | 84.5 | 84.1 |
| CC-OCR | 79.8 | 73.0 | 66.6 | 62.7 | 68.7 | 68.7 |
| OCRBenchV2 | 61.5 | — | 46.5 | 45.2 | 47.8 | 47.8 |
General Visual QA
| Benchmark | Qwen2.5-VL 72B | Gemini-2 | GPT-4o | Claude 3.5 | Qwen2-VL | Other Best |
|---|
| MegaBench | 51.3 | 55.2 | 54.2 | 52.1 | 46.8 | 47.4 |
| MMStar | 70.8 | 69.4 | 64.7 | 65.1 | 68.3 | 69.5 |
| MMBench1.1 | 88.0 | 83.0 | 82.1 | 83.4 | 86.6 | 87.4 |
Math & Reasoning
| Benchmark | Qwen2.5-VL 72B | Gemini-2 | GPT-4o | Claude 3.5 | Qwen2-VL | Other Best |
|---|
| MathVista | 74.8 | 73.1 | 63.8 | 65.4 | 70.5 | 72.3 |
| MathVision | 38.1 | 41.3 | 30.4 | 38.3 | 25.9 | 32.2 |
Video Understanding
| Benchmark | Qwen2.5-VL 72B | GPT-4o | Claude 3.5 | Qwen2-VL | Other Best |
|---|
| VideoMME | 73.3 | 71.9 | 60.0 | 71.2 | 72.1 |
| MMBench-Video | 2.0 | 1.7 | 1.4 | 1.7 | 1.9 |
| LVBench | 47.3 | 30.8 | — | — | 43.6 |
| CharadesSTA | 50.9 | 35.7 | — | — | 48.4 |
Visual Agent & Control
| Benchmark | Qwen2.5-VL 72B | GPT-4o | Claude 3.5 | Other Best |
|---|
| AITZ | 83.2 | 35.3 | — | 53.3 |
| Android Control | 67.4 | — | — | 66.4 |
| ScreenSpot | 87.1 | 18.1 | 83.0 | 89.5 |
| ScreenSpot Pro | 43.6 | — | 17.1 | 38.1 |
| AndroidWorld | 35.0 | 34.5 | 27.9 | 46.6 |
| OSWorld | 8.8 | 5.0 | 14.9 | 22.7 |
Step-by-Step Process to Install Qwen 2.5 VL Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1 x RTX A6000 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy Qwen 2.5 VL on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install Qwen 2.5 VL on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Install Ollama
After connecting to the terminal via SSH, it’s now time to install Ollama from the official Ollama website.
Website Link: https://ollama.com/
Run the following command to install the Ollama:
curl -fsSL https://ollama.com/install.sh | sh
Step 9: Serve Ollama
Run the following command to host the Ollama so that it can be accessed and utilized efficiently:
ollama serve
Now, “Ollama is running.”
Step 10: Select Qwen 2.5 VL Model
Link: https://ollama.com/library/qwen2.5vl
Qwen 2.5 VL model is available in four sizes: 3b, 7b, 32b and 72b. We will run it one by one on our GPU virtual machine.
Step 11: Connect with SSH
Now, open a new tab in the terminal and reconnect using SSH.
Step 12: Check Commands
Run, the following command to see a list of available commands:
ollama
Step 13: Run Qwen 2.5 VL 3b Model
Run the following command to interact with Qwen 2.5 VL 3b Model:
ollama run qwen2.5vl:3b
Step 14: Run Qwen 2.5 VL 7b Model
Run the following command to interact with Qwen 2.5 VL 7b Model:
ollama run qwen2.5vl:7b
Step 15: Run Qwen 2.5 VL 32b Model
Run the following command to interact with Qwen 2.5 VL 32b Model:
ollama run qwen2.5vl:32b
Step 16: Run Qwen 2.5 VL 72b Model
Run the following command to interact with Qwen 2.5 VL 72b Model:
ollama run qwen2.5vl:72b
Conclusion
Qwen2.5-VL is a massive leap in vision-language intelligence—and thanks to NodeShift, running it is now easier, faster, and more affordable than ever. Whether you’re testing the lightweight 3B model or powering up the full 72B version, NodeShift’s GPU Nodes give you the flexibility to choose the exact compute setup you need.
From OCR and diagram reading to visual reasoning and mobile UI understanding, Qwen2.5-VL performs like a model that actually sees. And paired with NodeShift’s GPU infrastructure, it becomes accessible to developers, researchers, and teams of all sizes—without breaking the bank.
If you’re looking to deploy cutting-edge multimodal models with zero hassle, spin up a GPU Node on NodeShift, install Ollama, and let Qwen2.5-VL do the rest.
