Docs
...
Default hardware configurations for deployments

Managing hardware specifications for deployments

Last updated: Feb 12, 2025
Managing hardware specifications for deployments

When you deploy certain assets in watsonx.ai Runtime, you can choose the type, size, and power of the hardware configuration that matches your computing needs.

Creating hardware specifications for deployments

You can create hardware specifications for your deployments in the following ways:

  • Python client library: Use the hardware_specifications.store function from the Python client library. For more information, see Python client library reference
  • Data and AI Common Core API: Use POST /v2/hardware_specifications from the Environments list in the Data and AI Common Core API to create a hardware specification. For more information, see Environments API reference.

Deployment types that require hardware specifications

Selecting a hardware specification is available for all batch deployment types. For online deployments, you can select a specific hardware specification if you're deploying:

  • Python Functions
  • Tensorflow models
  • Models with custom software specifications

Hardware configurations available for deploying assets

  • XS: 1x4 = 1 vCPU and 4 GB RAM
  • S: 2x8 = 2 vCPU and 8 GB RAM
  • M: 4x16 = 4 vCPU and 16 GB RAM
  • L: 8x32 = 8 vCPU and 32 GB RAM
  • XL: 16x64 = 16 vCPU and 64 GB RAM

You can use the XS configuration to deploy:

  • Python functions
  • Python scripts
  • R scripts
  • Models based on custom libraries and custom images

For Decision Optimization deployments, you can use these hardware specifications:

  • S
  • M
  • L
  • XL

Learn more

Parent topic: Managing predictive deployments