I dont know, there's been a load of progress in the 'run something like chatgpt on your own machine' dept in the last few months. Also Stanford trained Alpaca - fairly cheaply - using output from OpenAIs text-davinci-003, which somewhat suggests that the 'little guys' are are able to benefit from the expensive training done by the 'big guys' by using the big expensive models to train the small open-sources ones - https://crfm.stanford.edu/2023/03/13/alpaca.html

They're using specialized hardware to accelerate their development feedback loop. Without a doubt researchers and hackers will find ways to cut down model sizes and complexity, to run on consumer hardware, soon enough. Just use stable diffusion as an example: 4GB for the whole model. Even if text models are 16GB that'd be great.

I'm not sure what "open source" even means in the context of trained ML model. No one's going to be downloading this to their Macbook even if OpenAI would let you.

As for "non-OpenAI version", I'm not sure that it's OpenAI's fault that Google has missed a few steps here. It really SHOULD be them leading this field, if they weren't so fat and lazy. OpenAI is a 7-year old startup with just over a few hundred employees. This stuff is RIGHT THERE to be claimed by any players with access to funding and an ability to get out of their own way.

I'm drawn to disliking OpenAI for not being open, but on the other hand, as long as the architectures and techniques are public, progress will continue fast. If OpenAI drops the ball and stops improving, another company would just take their place.

Edit: never mind. "Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar."