That assumes demand remains constant. Maybe lowering the overall cost to train a model will mean that more people want to train models, thus raising the demand.
There are a lot of folks lining up to use a smart model. This takes tokens. I’m not convinced nvidia is blown up by this news at all. The trade has become less crowded, that is true.
if things become too efficient then you can use commodity compute and don't need GPUs at all. I'm not sure why you would think breakthroughs in efficiency would be good for nvidia. eventually a regular mac or PC will be able to do what you need a H100 to do now. that won't be good for nvidia.
My bet is there's no such thing as 'too efficient' in this space. If you can get a very good model on a small device, it's going to be totally amazing on a huge GPU.
I bet you're wrong - there are already massive diminishing returns in the best models from 2024 vs 2023. This idea that you can just through more compute and it scales with performance is fiction. You do get more performance with more compute, but it doesn't scale, and it's a waste of money, as shown with deepseek.
this conversation reminds me of people when the PS2 came out saying that by 2010 games would look literally better than real life, because they thought graphics quality would exponentially improve...
I would agree except I think it’s 1997. I’ve gotten DeepSeek to mostly solve a not very complex problem in a somewhat obscure domain (home assistant automations) in 214 seconds of its own ‘thinking’ and if you can get this two orders of magnitude lower this unlocks completely new use cases ie. demand.
