Cost Efficiency (Open Source)
Lower Long Term costs
Customised data control
Pre-trained model
Get Your OpenThinker 7B AI Model Running in a Day
Deploying OpenThinker 7B on Microsoft Azure using Virtual Machines (VMs) is a straightforward approach that offers full control over the infrastructure. We will:
Deploy an Azure Virtual Machine (VM)
Install Docker on the VM
Pull and run the OpenThinker 7B model as a Docker containerConfigure networking for external access
Before starting, ensure you have:
An active Azure subscription
Azure CLI installed
Docker installed on your local machine
A pre built Docker image of OpenThinker 7B
Log in to Azure CLI
az login
If you have multiple subscriptions, set the active subscription:
az account set --subscription "your-subscription-id"
Create a Resource Group
az group create --name OpenThinkerRG --location eastus
This creates a resource group named OpenThinkerRG in the East US region.
Create an Azure Virtual Machine
az vm create \
--resource-group OpenThinkerRG \
--name OpenThinkerVM \
--image UbuntuLTS \
--size Standard_NC4as_T4_v3 \
--admin-username azureuser \
--generate-ssh-keys
This creates an Ubuntu VM with an NVIDIA T4 GPU (adjust VM size if needed).
SSH keys are automatically generated for secure access.
Connect to the VM via SSH
ssh azureuser@<public-ip-address>
Find your VM’s public IP using:
az vm list-ip-addresses --resource-group OpenThinkerRG --name OpenThinkerVM --output table
Update System and Install Docker
Once inside the VM, update and install Docker:
sudo apt update && sudo apt upgrade -y
sudo apt install -y docker.io
Enable Docker Service
sudo systemctl enable docker
sudo systemctl start docker
Verify Docker Installation
docker --version
Expected output:
Docker version <docker version>
Pull the Docker Image from Docker Hub or Azure Container Registry (ACR)
If the model image is on Docker Hub:
docker pull your-dockerhub-username/openthinker-7b:latest
If the image is in Azure Container Registry (ACR):
Login to ACR
az acr login --name OpenThinkerRegistry
Retrieve the ACR Login Server
az acr show --name OpenThinkerRegistry --query loginServer --output tsv
Pull the image
docker pull <acr-login-server>/openthinker-7b:latest
Run OpenThinker 7B Container
docker run -d --name openthinker-7b -p 80:11434 <acr-login-server>/openthinker-7b:latest
Allow Traffic on Port 80
By default, Azure VMs block external traffic. Open port 80:
az vm open-port --port 80 --resource-group OpenThinkerRG --name OpenThinkerVM
Check Running Containers
docker ps
Expected output:
CONTAINER ID IMAGE PORTS STATUS
[Container ID] openthinker-7b:latest 0.0.0.0:80->11434/tcp Up X minutes
Find the Public IP of the VM
az vm list-ip-addresses --resource-group OpenThinkerRG --name OpenThinkerVM --output table
Test API Response
Use cURL or a web browser to check if the model is running:
curl http://<public-ip>
Expected output:
{"message": "Model is up and running"}
Ensure the container starts automatically after a reboot:
Create a Systemd Service
sudo nano /etc/systemd/system/openthinker.service
Paste the following:
[Unit]
Description=OpenThinker 7B AI Model
After=network.target
[Service]
ExecStart=/usr/bin/docker start -a openthinker-7b
ExecStop=/usr/bin/docker stop openthinker-7b
Restart=always
User=root
[Install]
WantedBy=multi-user.target
Enable the Service
sudo systemctl daemon-reload
sudo systemctl enable openthinker.service
sudo systemctl start openthinker.service
If you need more computing power, consider:
Upgrading to a more powerful VM (e.g., Standard_NC8as_T4_v3)
Using multiple VMs with a Load Balancer
Running multiple containers on the same VM
To run multiple containers:
docker run -d --name openthinker-7b-2 -p 81:11434 <acr-login-server>/openthinker-7b:latest
This runs a second instance on port 81.
To stop the VM:
az vm stop --resource-group OpenThinkerRG --name OpenThinkerVM
To delete the VM:
az vm delete --resource-group OpenThinkerRG --name OpenThinkerVM --yes
To delete the resource group (removes all related resources):
az group delete --name OpenThinkerRG --yes --no-wait
Deploying OpenThinker 7B on an Azure Virtual Machine provides a flexible and controlled environment. By using Docker, we can quickly set up and run the model, exposing it over the internet with minimal configuration.
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