If I were Apple I'd be thinking about the following issues with that strategy:
1. That RAM isn't empty, it's being used by apps and the OS. Fill up 64GB of RAM with an LLM and there's nothing left for anything else.
2. 64GB probably isn't enough for competitive LLMs anyway.
3. Inferencing is extremely energy intensive, but the MacBook / Apple Silicon brand is partly about long battery life.
4. Weights are expensive to produce and valuable IP, but hard to protect on the client unless you do a lot of work with encrypted memory.
5. Even if a high end MacBook can do local inferencing, the iPhone won't and it's the iPhone that matters.
6. You might want to fine tune models based on your personal data and history, but training is different to inference and best done in the cloud overnight (probably?).
7. Apple already has all that stuff worked out for Siri, which is a cloud service, not a local service, even though it'd be easier to run locally than an LLM.
And lots more issues with doing it all locally, fun though that is to play with for developers.
I hope I'm wrong, it'd be cool to have LLMs be fully local, but it's hard to see situations where the local approach beats out the cloud approach. One possibility is simply cost: if your device does it, you pay for the hardware, if a cloud does it, you have to pay for that hardware again via subscription.