ModelMesh Serving includes some built-in ServingRuntimes for common ML frameworks, but also supports custom runtimes. Custom runtimes are created by building a new container image with support for the desired framework and then creating a ServingRuntime custom resource using that image.
If the desired custom runtime uses an ML framework with Python bindings, there is a simplified process to build and integrate a custom cuntime. This approach is detailed in the Python-based Custom Runtime on MLServer page.
In general, the implementation of a complete runtime requires integration with the Model Mesh API as detailed on the Custom Runtimes page.