Google has a family of local models too! https://ai.google.dev/gemma/docs
replyGemma and Llama can’t be bundled commercially, which sucks because they make two of the leading small llms. Qwen3 might be the last one with an Apache license.
replyYou can bundle and use Gemma commercially[1].
replyI’ll have to read that, thanks.
replyIt's very convenient for Apple to do this: less expenses on costly AI chips, and more excuses to ask customers to buy their latest hardware.
replyUsers 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.
Well, unless it's open source, you can't be so certain. But more certain than when processing in the cloud, that's true.
replyIf iPhones were the efficient/smart way to pay for compute then Apple's datacenter would be built with those instead of servers.
replyBut 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.
Or, instead of improving efficiency, they go ahead and just deploy more generators [0]. Stop gap measures are cheaper.
reply[0] https://interestingengineering.com/innovation/elon-musk-xai-...
Well, if it were easier to build power stations, they'd do so.
replyThe 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.
replyHow does running AI workloads on end user devices magically make them use less energy?
replyMore like squinting to see if it's still visible in the rear view mirror.
replyWith the wave of enshitiffication that's surrounding everything tech or tech-adjacent, the privacy cost is pretty~ high.
replyIt takes about a $400 dollar graphics card to comfortably run something like a 3b-8b model. Comfortable as in fast inference, good sized context. 3b-5b models are what devices can somewhat fit. That means for us to get good running local models, we’d have to shrink one of those $400 dollar graphics cards down to a phone.
replyI don’t see this happening in the next 5 years.
The Mac mini being shrunk down to phone size is probably the better bet. We’d have to bring down the power consumption requirements too by a lot. Edge hardware is a ways off.
Gemma 3n E4B runs at 35tk/s prompt processing and 7-8 tk/s decode on my last last last gen flagship Android.
replyI doubt this. What kind of t/s are you getting once your context window is reasonably saturated? Probably slows down to a crawl making it not good enough yet (the hardware that is).
replyWith no company having a clear lead in everyday ai for the non technical mainstream user, there is only going to be a race to the bottom for subscription and API pricing.
replyLocal doesn't cost the company anything, and increases the minimum hardware customers need to buy.
> Local doesn't cost the company anything, [...]
replyNot completely true: those models are harder to develop. The logistics are a hassle.