> For the complete documentation index, see [llms.txt](https://docs.kaisar.io/kaisar-network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kaisar.io/kaisar-network/kaisar-architecture/products/kaisar-cloud-end-users/how-to-deploy-a-gpu-container.md).
# How to deploy a GPU Container
1. Navigate to the **"Deploy GPU Container"** button(located on upper right in screenshot below) under **GPU Container** page.
2. **Package Details:** Review the specifications of GPU container such as GPU cores, CPU cores, and memory before deployment.
3. Customize your deployment by configuring environment variables, exposed ports, and other necessary parameters.
4. Choose GPU container instance pricing according to your needs. Kaisar offers various pricing models such as on-demand, 1 month, 3 months, 6 months, 12 months, and 24 months with corresponding hourly rates and total costs for reservation.
*See screenshot below for reference.*
5. Check all configurations before finalizing the deployment. Ensure all details are correct and meet your requirements:
**Container Summary:** Provides a summary of the selected GPU instance, including GPU model, VRAM, RAM, vCPU, connectivity speed, and location.\
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**Order Summary:** Shows the total cost for GPU on-demand usage and cloud services fee.\
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**Payment Method:** Options to pay with USDT or credit card (coming soon).\
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**Proceed to Payment Button:** To finalize and proceed with the payment.\
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*See Screenshot below for reference.*
6. After deployment, monitor the container details, including CPU, memory, network traffic, logs, status, uptime, and logs.
7. Click on the **"Connect"** button to view access details for the container.
8. Now access the GPU container using the provided credentials and run AI workflows.
