Gemma 4 on iPhone
https://apps.apple.com/nl/app/google-ai-edge-gallery/id6749645337
Gemma 4 on iPhone
https://apps.apple.com/nl/app/google-ai-edge-gallery/id6749645337
OP Here. It is my firm belief that the only realistic use of AI in the future is either locally on-device for almost free, or in the cloud but way more expensive then it is today.
The latter option will only bemusedly for tasks that humans are more expensive or much slower in.
This Gemma 4 model gives me hope for a future Siri or other with iPhone and macOS integration, “Her” (as in the movie) style.
> or in the cloud but way more expensive then it is today.
Why? It's widely understood that the big players are making profit on inference. The only reason they still have losses is because training is so expensive, but you need to do that no matter whether the models are running in the cloud or on your device.
If you think about it, it's always going to be cheaper and more energy-efficient to have dedicated cloud hardware to run models. Running them on your phone, even if possible, is just going to suck up your battery life.
> It's widely understood that the big players are making profit on inference.
Are they? Or are they just saying that to make their offerings more attractive to investors?
Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Locally running models that are snappy and mostly as capable as current sota models would be a dream. No internet connection required, no payment plans or relying on a third party provider to do your job. No privacy concerns. Etc etc.
You can pick models that are snappy, or models that are as capable as SOTA. You don't really get both unless you spend extremely unreasonable amounts of money on what is essentially a datacenter-scale inference platform of your own, meant to service hundreds of users at once. (I don't care how many agent harnesses you spin up at once, you aren't going to get the same utilization as hundreds of concurrent users.)
This assessment might change if local AI frameworks start working seriously on support for tensor-parallel distributed inference, then you might get away with cheaper homelab-class hardware and only mildly unreasonable amounts of money.