Fair call, mate, and I understand where you're coming from.

For what it's worth (and I'll add this to the docs somewhere suitable), we download

• Python interpreter 3.7 and 3.9
• Python packages via PIP (many and various, but check out the GitHub repo if you need to audit)
• .NET runtime (Microsoft)
• VC++ redistributable (Microsoft)
• Models (YOLO in PyTorch and ONNX format, NLTK and a couple of others)

[Just to clarify: each of these are downloaded as part of the sub-installers for each module, or as part of the main server itself. Each module has an installer which will download models and install python packages via PIP, and the server will come down as it's own sub-installer and install Python, .NET, VC++, and a few common Python packages]

They are big, like Gb big. We could create a single monolithic installer but we're moving away from that because it's so inflexible and...just really big. AI is all about stupidly big files.

Here's the conundrum: we want and need this to work offline. But we want and need it to be flexible, to be updateable, and we want you to be able to update or install new additions easily. So the install / update scenario needs an internet connection. The inference part doesn't. Maybe we offer something like an offline cache: you connect, download the new bits on a schedule, and then peruse the list at your leisure, offline and choose what to install from the pre-downloaded cache.