Most recent news about AI seems to involve staggering amounts of money. OpenAI and Nvidia sign a $100b data center contract. Meta offers researchers $100m salaries. VCs invested almost $200b in AI …
This is the real future of neural networks. Trained on supercomputers - runs on a Game Boy. Even in comically large models, the majority of weights are negligible, and local video generation will eventually be taken for granted.
Probably after the crash. Let’s not pretend that’s far off. The big players in this industry have frankly silly expectations. Ballooning these projects to the largest sizes money can buy has been illustrative, but DeepSeek already proved LLMs can be dirt cheap. Video’s more demanding… but what you get out of ten billion weights nowadays is drastically different from a six months ago. A year to date ago, video models barely existed. A year to date from now, the push toward training on less and running on less will presumably be a lot more pressing.
I’m very interested in this approach because I’m heavily constrained by money. So I am gonna be looking (in non appliance contexts) to develop workflows where genAI can be useful when limited to small models running on constrained hardware. I suspect some creativity can yield useful tools with these limits, but I am just starting out.
This is the real future of neural networks. Trained on supercomputers - runs on a Game Boy. Even in comically large models, the majority of weights are negligible, and local video generation will eventually be taken for granted.
Probably after the crash. Let’s not pretend that’s far off. The big players in this industry have frankly silly expectations. Ballooning these projects to the largest sizes money can buy has been illustrative, but DeepSeek already proved LLMs can be dirt cheap. Video’s more demanding… but what you get out of ten billion weights nowadays is drastically different from a six months ago. A year to date ago, video models barely existed. A year to date from now, the push toward training on less and running on less will presumably be a lot more pressing.
I’m very interested in this approach because I’m heavily constrained by money. So I am gonna be looking (in non appliance contexts) to develop workflows where genAI can be useful when limited to small models running on constrained hardware. I suspect some creativity can yield useful tools with these limits, but I am just starting out.