Users have to pay for the compute somehow. Maybe by paying for models run in datacenters. Maybe paying for hardware that's capable enough to run models locally.
replyI can upgrade to a bigger LLM I use through an API with one click. If it runs on my device device I need to buy a new phone.
replyI* can run the model on my device, no matter if I have an internet connection, nor if I have a permission from whoever controls the datacenter. I can run the model against highly private data while being certain that the private data never leaves my device.
replyIt's a different set of trade-offs.
* Theoretically; I don't own an iPhone.
But also: if Apple's way works, it’s incredibly wasteful.
replyServer side means shared resources, shared upgrades and shared costs. The privacy aspect matters, but at what cost?
Server side means an excuse to not improve model handling everywhere you can, and increasing global power usage by noticable percentage point, at a time when we're approaching "point of no return" with burning out the only planet we can live on.
replyThe cost, so far, is greater.
> Server side means an excuse to not improve model handling everywhere you can...
replyHow so if efficiency is key for datacenters to be competitive? If anything it's the other way around.
The previous commenter is right in that server-side companies have little incentive to do less, especially when they're backed by investors money. Client-side AI will be bound by device capabilities and customer investment in new devices.
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