In the fast-paced industry of AI, where models are no longer just tools but collaborators in reasoning, coding, and agentic decision-making, GLM-4.6 emerges as a significant advancement. Building upon the strengths of GLM-4.5, this latest release expands the context window to a staggering 200K tokens, enabling it to handle far more intricate tasks, from multi-step reasoning to complex code generation. Its superior coding performance shines across real-world benchmarks such as Claude Code, Cline, Roo Code, and Kilo Code, producing not only functionally correct solutions but visually polished front-end interfaces as well. GLM-4.6 demonstrates significant improvements in advanced reasoning and tool-assisted inference, making it a powerhouse for intelligent agent frameworks and search-based applications. Beyond raw capability, the model’s refined writing style aligns more naturally with human preferences, delivering outputs that are coherent, contextually rich, and suitable for role-playing or content creation scenarios. Evaluated across eight public benchmarks spanning reasoning, coding, and agentic tasks, GLM-4.6 consistently outperforms GLM-4.5 and holds its own against top international models like DeepSeek-V3.1-Terminus and Claude Sonnet 4, offering both versatility and reliability for developers and researchers alike.
This guide will show you how to quickly install and run GLM-4.6 on your local machine with GPU enabled environment.
Prerequisites
The minimum system requirements for running this model are:
Step-by-step process to install and run GLM 4.6
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 8X H200 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 2 TB 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 8x H200 140 GB GPU node with 192vCPUs/3TB RAM/2TB 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 glm python=3.11 -y && conda activate glm
Output:
2. Run the following command to install vllm and any other remaining packages needed to run vllm and not installed already.
pip install --upgrade vllm
Output:
3. Install required dependencies.
pip install transformers>=4.56.2 pre-commit>=4.2.0 accelerate>=1.10.1
Step 8: Download and Run the model
- Start the vllm server with this command that will also download the model.
vllm serve zai-org/GLM-4.6 \
--tensor-parallel-size 8 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--served-model-name glm-4.6
Output:
2. Open another new terminal, and connect it to same remote server using SSH, and hit this curl command to send API request to our running vllm server.
(Make sure the vllm server is up and running in another terminal on the same remote server)
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.6",
"prompt": "Write a short explaination about black whole.",
"max_tokens": 100,
"temperature": 0.7
}'
Output:
Conclusion
GLM-4.6 provides amazing AI capabilities, combining extended context windows, advanced reasoning, superior coding performance, and more natural, human-aligned outputs. It empowers developers and researchers to build stronger agentic frameworks, generate high-quality code, and tackle complex reasoning tasks with ease. Running GLM-4.6 on NodeShift Cloud further enhances this experience by providing seamless deployment, scalable resources to manage intensive workloads, and reliable infrastructure that ensures your AI applications perform at their best, making it simple to harness the full potential of this powerful model in real-world projects.
