> One thought that occurred to me after this investigation was the premise that the immense bleeding-edge models of today with billions of parameters might be able to be built using orders of magnitude fewer network resources by using more efficient or custom-designed architectures.
I'm pretty convinced that something equivalent to GPT could run on consumer hardware today, and the only reason it doesn't is because OpenAI has a vested interest in selling it as a service.
It's the same as Dall-E and Stable Diffusion - Dall-E makes no attempt to run on consumer hardware because it benefits OpenAI to make it so large that you must rely on someone with huge resources (i.e. them) to use it. Then some new research shows that effectively the same thing can be done on a consumer GPU.
I'm aware that there's plenty of other GPT-like models available on Huggingface, but (to my knowledge) there is nothing that reaches the same quality that can run on consumer hardware - yet.
I'm pretty convinced that something equivalent to GPT could run on consumer hardware today, and the only reason it doesn't is because OpenAI has a vested interest in selling it as a service.
It's the same as Dall-E and Stable Diffusion - Dall-E makes no attempt to run on consumer hardware because it benefits OpenAI to make it so large that you must rely on someone with huge resources (i.e. them) to use it. Then some new research shows that effectively the same thing can be done on a consumer GPU.
I'm aware that there's plenty of other GPT-like models available on Huggingface, but (to my knowledge) there is nothing that reaches the same quality that can run on consumer hardware - yet.