Well, corporate organizations developing LLMs and open source LLMs are not mutually exclusive. LLMs more lightweight so as to run on more mainstream hardware, b

Well, corporate organizations developing LLMs and open source LLMs are not mutually exclusive. LLMs more lightweight so as to run on more mainstream hardware, but not as capable as corporate ones, may still be very useful. Running the software on site has many advantages as you outlined above, by highlighting the disadvantages. The OpenAssistant was/is trained on well structured data from humans for exactly that purpose, for deep learning. In the past most LLMs were trained on unstructured internet data, and they performed well enough. But it was only when OpenAI used reinforcement learning that really the model started to shine. In my opinion well structured data as input to the machine, have a long way to go. More lightweight models, a lot more precise, a lot faster execution and a lot less memory usage are certainly possible. Most probably we are at the end of the road for the usefulness of structured data. I remember reading an article "Why Large Language models are over", meaning that smaller models but better trained, with better data and algorithms are the way to go.