I'm sorry, but we're talking about "on edge" here though. Those other companies have no flipping hardware to run it "on edge", in a "generic" way, which is the goal. Apple's strategy involves the generic.
> If apple wants to use open weights
This doesn't make sense. Apple doesn't dictate the models you can use with their hardware. You can already accelerate LLAMA with the neural engines. You can download the app right now. You can already deploy your models on edge, on their hardware. That is the success they're achieving. You cannot effectively do this on competitor hardware, with good performance, from "budget" to "Pro" lineup, which is a requirement of the goal.
> they will still be behind other companies have done model distillation and federated learning for years.
What hardware are they running it on? Are they taking advantage of Apple (or other) hardware in their strategy? Federated learning is an application of "on edge", it doesn't *enable* on edge, which is part of Apple's strategy.
> Ask apple's newly poached AI hires this question. Doesn't seem like you would take an answer from me.
Integrating AI in their apps/experience is not the same as enabling a generic "on edge", default, capability in all Apple devices (which they have been working towards for years now). This is the end goal for "on edge". You seem to be talking about OS integration, or something else.
> People have been running distilled llamas on rPis with pretty good throughput.
Yes, the fundamental limitation there being hardware performance, not the model, with that "pretty good" making the "pretty terrible" user experience. But, there's also nothing stopping anyone from running these distilled (a requirement of limited hardware) models on Apple hardware, taking advantage of Apples fully defined "on edge" strategy. ;) Again, you can run llamas on Apple silicon, accelerated, as I do.