Let's see if I understand:

• Triton server deployments only have manual, static deployment of models for inferencing (without enterprise)
• ClearML can load and unload models based upon usage, but has to do so from the hard drive
• Triton server does not support saving models off to normal RAM for faster loading/unloading
• Therefore, currently, we can deploy 100 models when only 5 can be concurrently loaded, but when they are unloaded/loaded (automatically by ClearML), it will take a few seconds because it is being read from the the SSD, depending on the size.
  If this is the case, that should be acceptable for our application.