GPU Instance provides cost-effective, accessible high-performance computing with easy GPU access, making it ideal for complex AI tasks. Users benefit from on-demand rental, optimizing both efficiency and cost-effectiveness.

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Features

  • Rich built-in templates help you quickly deploy GPU instances for AI tasks out of the box;
  • Start a GPU instance in seconds, helping you quickly handle traffic peaks;
  • Provides nodes in multiple global regions to deploy GPUs closer to your users for minimal latency;
  • Provides free local storage and allows you to attach network volume, also provides free network transfer;
  • Billing is accurate to the second, charging only for the actual running time of the GPU container instances;
  • Affordable pricing helps you save costs by up to 50% easily.

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Terminology

  • Container Image: Docker-compatible OCI image reference, supporting both public registries and private repositories with authentication.
  • Environment Variables: Runtime configuration key-value pairs injected into containers for dynamic configuration and secrets management.
  • HTTP/TCP Ports: Network endpoints exposed by containers for communication. HTTP ports (e.g., 80, 443) for web services, TCP ports for general network protocols.
  • Container Disk: Root filesystem partition containing OS and system binaries. Mounted as read-write at container startup.
  • Volume Disk: High-performance ephemeral storage directly attached to the GPU instance. Optimized for I/O intensive workloads but non-persistent across instance restarts.
  • Network Volume: Network-attached storage service that allows users to access and manage data through a network. Data can be stored on remote servers and accessed from any device.

If you have any questions, you can first check the FAQs for GPU Instance.