> For the complete documentation index, see [llms.txt](https://docs.console.zenlayer.com/welcome/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.console.zenlayer.com/welcome/gpu-cloud/virtual-machines/create-a-gpu-instance.md).

# Create a GPU Instance

## Procedures

1. Log in to [**zenConsole**](https://console.zenlayer.com/), go to **Products** > **Virtual Machine** > **GPU & AI** > **GPU Cloud**, and click **Create GPU Instance**.
2. **Select a location**.\
   Choose the closest region for optimal performance.
3. **Choose your use case and image**.\
   Choose an available pre-built environment to quickly set up the instance with essential tools.
   * **AI/ML Inference**\
     Ollama image preinstalled with: `Llama 3.1:8b`, `NvidiaDriver 550.90.07`, `CUDA 12.6`, `cuDNN 9.3.0`, `Python 3.12.3`, `Ollama 0.3.9`, `JupyterLab 4.2.5`. Used for deploying models and running inference tasks.
   * **Common Use**\
     Ubuntu Server 24.04 LTS image preinstalled with: `NvidiaDriver 550.90.07`, `CUDA 12.6`, `cuDNN 9.3.0`. Used for common computing needs.
4. **Select a GPU model**.\
   Opt for the **NVIDIA GeForce RTX 4090** with 24 GiB of vRAM, ensuring high-performance computation with **82+ TFLOPS** of FP32 compute power, perfect for demanding AI tasks.
5. **Configure storage**.

   Set your storage requirements, basic and standard NVMe SSDs for fast I/O operations.
6. **Manage instance access**.
   * Add an **SSH key pair** to securely access your instance.
   * Use the default user **"ubuntu"** or create a custom password for login.
7. **Choose quantity**.\
   Set the number of instances you want to create and give the instance a **resource name**.
8. **More settings**.
   * Select your **OS time zone**.
   * Assign your resources to your desired **resource group**.
9. **Confirm order**.\
   Check the order summary and confirm your order.

## Key Considerations

1. **Location and Latency**: Choosing the right region is important for reducing latency. Select a region closest to your user base or data source.
2. **Storage Allocation**: Ensure you allocate sufficient NVMe SSD storage based on your workload requirements. If working with large datasets, you might need to expand storage.
3. **Security**:
   * Recommended to use SSH key pairs for secure access.
   * Regularly update your software and patches to maintain security.