Two things stand out. Stable Diffusion; the weights for it and the supporting Python code was open sourced and released to the public. Anyone can find the cpkt file and the Python code online and download it and (with a bit of work) run it on their own computer if they have the hardware (any reasonable GPU) for it. There has been no such release by OpenAI, the closest we got is Facebook's leaked LLama model, and that's not a chat bot. So we don't have a model to run. Stability AI/Emad paid to train the model, which cost like a half million dollars in GPU time (he obviously didn't pay the retail price of $600k, but also it's not something you'd get right on the first shot either) and then gave the output from that away. It's not clear how much it would cost to train a comparable chat bot to ChatGPT but the impression is that would take much more.
The second thing is that it's not clear that we, the Internet at large would actually benefit from the model's release. StableDiffusion is 4 gigs and able to run on all sorts of consumer grade hardware, leading to such a Renaissance. ChatGPT makes liberal use of Nvidia A100 GPUs that are available to them to use as compute in Azure. (AWS and GCP, along with many AI focused smaller cloud companies also offer these.) One of those costs, like, $10k. And you need several of them to be able to run ChatGPT. Which means even if OpenAI were to live up to their name and release ChatGPT's model, only businesses and research labs would actually have the hardware to run it, so it would be awesome to have the weights, but you wouldn't have the same army of developers able to work on it.
There are open source LLM chat bots out there, so I think we will see one become popular, but at least that says why it'll be a second before we do.
I have been fooling around with the small 7B llama models. They chat, but they are pretty dumb compared to ChatGPT. This means they are terser and they confabulate more, even for things that are common knowledge. It seems, from asking it questions about current events, that the model was trained up to data from early 2020.
I haven't seen much output yet from the biggest 65B parameter llama model. One can rent cloud VMs that can run it for $1.25 an hour or so on vast.ai to run it, but ChatGPT is $20 a month so why bother, unless you like the fully uncensored aspect.
Most AI art generators are run on single GPU and can be trained with some top-of-the-line consumer hardware. Expensive but accessible.
A full blown LLM like ChatGPT is literally the cost of a small startup to build and trained. Running it is near impossible without cards like A100 which alone costs more than a full enthusiast grade PC.
Maybe eventually they will distill and optimize the models so that we can fit these things on a PC, then laptop, and then phones. But for now it is exclusively the domain of big tech.
They are smaller models with less parameters. Their original small sizes relative to LLMs also let people play around with it and tune it to run on less expensive hardware, if the weights are given, ie open source like SD.
Originally SD was quite hard to run, with an 8GB high end card only outputting 256x256 images. Then AMD and NVIDIA started releasing 16GB and 24GB consumer cards and people start doing training on those GPUs and tuning their own models. Now we have plenty of cards and models that can do 512x512.
Stable Diffusion will run on any decent gaming GPU or a modern MacBook, meanwhile LLMs comparable to GPT-3/ChatGPT have had pretty insane memory requirements - e.g., <https://github.com/facebookresearch/metaseq/issues/146>
Worth noting that the M-series macbooks are UMA so 100GB VRAM is costly but easily accessible. Their GPU performance is nowhere near a 96GB A100, but for sheer VRAM it’s a good choice.
