Create a GPU Instance

Procedures

1. Log in to zenConsole, 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.