3 ms·
That's highly unlikely. I think most people can't fathom how huge is the scale of GPT-4 and what computing power it requires. Even if 10x optimizations, 10x per
by bitL 4y ago
That's highly unlikely. I think most people can't fathom how huge is the scale of GPT-4 and what computing power it requires. Even if 10x optimizations, 10x performance and 10x memory (highly unlikely in 5 years) that's not going to be sufficient to run it locally.
- yieldcrv 4y agoI think people are overestimating how many parameters GPT-4 is really trained on. While also overestimating how many parameters anybody really needs after fine tuning. The market will find the sweet spot. Right now everyone’s tinkering with the 7B parameter LLM and then going to move up to the 65B one once they've refined the process. I think its fiction that anybody really needs a 10 trillion parameter LLM at all. It will be completely niche.
- bitL 4y agoFine-tuning is a solved problem using transformer adapters which are fast, small and match regular fine-tuning, so that's not an issue. As for smaller models, their usefulness will quickly wane when they will be seen as producing trivial gibberish in the light of cloud models available in 5 years.