The Qwen3-30B-A3B-Instruct-2507 is an advanced iteration of the Qwen3 series, marking a significant leap forward in the landscape of causal language models. Boasting an impressive 30.5 billion parameters with 3.3 billion actively engaged, this model excels across a diverse array of capabilities such as instruction following, complex logical reasoning, text comprehension, mathematics, and science. Its robust coding proficiency, demonstrated by high scores in benchmarks such as MultiPL-E and LiveCodeBench, makes it particularly attractive to developers and researchers. The model also excels in multilingual contexts and handles extensive 256K token contexts effortlessly, making it ideal for intricate, lengthy tasks. Furthermore, its refined alignment with user preferences in subjective and open-ended scenarios ensures that interactions feel natural, intuitive, and highly personalised.
In this article, we guide you step-by-step on installing Qwen3-30B locally or in GPU-accelerated environment.
Prerequisites
The minimum system requirements for running this model are:
Step-by-step process to install and run Qwen3-30B
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 H100 SXM 80GB GPU node with 192vCPUs/80GB 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. Once you’re inside the environment, install vllm with dependencies.
pip install --upgrade vllm
Output:
3. Also, open a second terminal, connect to remote server with SSH and install open-webui.
pip install open-webui
Step 8: Download the model
- Download model with
vllm and host the endpoint at 8000.
vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 32768 --gpu-memory-utilization 0.95
Output:
2. In the second terminal connected with the GPU host with ssh, serve the open-webui frontend endpoint.
open-webui serve --port 3000
Output:
3. Forward both the ports and tunnel them to access in the local browser.
If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the both vllm and open-webui 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 3000:localhost:3000 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
In another local terminal run forward the port for vllm endpoint:
ssh -L 8000:localhost:8000 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Step 9: Run the model via Open WebUI Interface
Once ports are forwarded, you can simply access the model via Open WebUI interface and chat with it.
- Before running the model, connect the webui with
vllm API endpoint in the settings.
2. Select the Qwen3-30B model in the chat page and run the prompt.
For e.g., we’re testing the following prompt:
1. Summarize the following passage in 3 bullet points.
2. Then, extract 3 key insights and explain their implications.
3. Finally, write a Python function that could analyze similar passages for sentiment.
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Passage:
"The rapid advancement of AI technologies has transformed industries across the globe. In healthcare, AI models are diagnosing diseases earlier and more accurately. In finance, algorithmic trading and risk modeling are becoming more sophisticated. Yet, as AI grows more powerful, ethical questions around bias, privacy, and job displacement remain urgent. Policymakers and technologists must collaborate to create guardrails that ensure innovation benefits society as a whole."
---
Give your response in clearly separated sections.
Output:
Conclusion
Installing Qwen3-30B-A3B-Instruct-2507 locally equips developers and researchers with a cutting-edge language model, renowned for its powerful reasoning, extensive multilingual support, and exceptional handling of long-context tasks. Pariring it with NodeShift GPUs further enhances this experience, providing streamlined deployment, efficient resource management, and scalable infrastructure. Together, these tools empower users to harness advanced AI capabilities effectively, bridging innovation with accessibility and performance.
