That's not how these LLMs work. There is a training phase which takes a large amount of compute power, and the training generates a model which is a set of weights and could easily be backed up and version-controlled. The model is then used for inference which is a less compute-intensive process and runs on much smaller hardware than the training phase.
The inference architecture does use feedback mechanisms but the feedback does not modify the model-weights that were generated at training time.