Multiple model Inference And Runtime Model Switching

Hello,
I want to perform inference using multiple models in my design on Jetson devices.
I came across this issue here, but it only addresses the scenario of multiple inputs for a single model.
I found that the Triton server has the capability of loading multiple models into the GPU simultaneously, but the "triton_node" only accepts a single const std::string model_name_.

I have the following questions that I would be grateful if someone could answer:
1- Should I create multiple TensorRT or Triton server engines in my ROS environment? Is it even possible or recommended?

In another scenario, I would like to switch between different models at runtime.

2- Since the model_name is provided as a parameter, is it possible to switch between different models at runtime without shutting down the DNN node? If not, what is the proper way of switching between models?

UPDATE:
Following this link and this link it seems like running tensorrt engine in multiprocess create two context and two context get scheduled in time slice fashion cause inference time to increase. so should i create a composable node out of all tensorrt engines so they created in multithreading mode and not multiprocessing ?
But in Nvidia github here, they simply run two launch files (stereo and unet) as seperate nodes. isn't it cause a time slice problem ?!

Start with the triton_node implementation and its model_name parameter, then review the linked multi-model and Nav2-with-Isaac-ROS-GEMs examples. Determine whether multiple models and runtime switching are supported, including the separate-node versus composable-node behavior. Done means documenting or specifying the supported approach and its performance implications.