201–210 of 396 posts
I was waiting for a MacBook Pro M6 Max and now I don’t know what to do, especially with the price increase I feel like I really screwed up not just getting an MBP M5 Max a month ago
Are you upgrading from a perfectly good machine? Then wait.
Former AnandTech editor Gavin Bonshor had reports that the M7 would be manufactured on Intel's 18A node. https://bontechlabs.com/news/apple-is-reportedly-using-intel... Given the risks involved in establishing Apple Silicon designs with a new fab, I would expect early M7 parts to be in test production right now. The fundamental M7 design is already set in stone. Mark Gurman's Bloomberg article does not mention fabric…
Wouldn't this help Intel compete? If they have Apple's designs months prior to launch, rather than after launch.
Earlier quoted context omitted.
I am waiting till apple copies the "allocation" concept from high end car manufacturers. "Sure, buy the 25 iphones ans we will gladly put you on the waitlist."
This makes no sense. Apple doesn't need to generate artificial demand for their products. Apple doesn't need (or want) a perception of exclusivity.
A top of range Mac is a depreciating asset and looks exactly the same as the other models physically.
Earlier quoted context omitted.
Indeed. Local models becoming available and halfway decent don't obviate the laws of scale. And because there's no ceiling to what scaling more will buy you in terms of capability, there's no reason not to scale more, there's no incentive for billionaires not to grab all the fab capacity they can. Enjoy paying $1000 or more for a little 4 GiB cloud terminal that connects you to all your online accounts where all your…
>there's no ceiling to what scaling more will buy you in terms of capability This is highly doubtful. Rule of thumb: everything people think is exponential is actually an S curve.
Earlier quoted context omitted.
> Though, I've been saying for a while that the local AI inflectiom point is the death knell for these frontier labs. "Death knell" is a touch hyperbolic. Hardware that can only run quantized models that take up GBs in VRAM falls short of even an A100 (by almost an order of magnitude[0]), which in turn falls short of what an 8xH100 cluster can do (also by another order of magnitude[0]). I'm an avid believer in local…
The big question for local LLMs is whether there is a 100 tok/s model which requires less than 16 GB of memory and is competitive on most tasks with the cloud models. There is some signal that this is possible through both hardware innovation and training/data improvements. Cloud models have their own constraints - I can’t have opus4.8 spend 4 hours on a deep research question I had in the shower without spending mon…
I'm not talking local Gemma/Qwen vs cloud Opus, but against OpenRouter same Gemma/Qwen
there are reasons to run local - privacy, availability, but cost is not one of them
Apple is actually interesting. They are one of the few companies with a chip / PC play with real power AND basically no play I'm the hyperscalar market. That means they're actually incentivized at least short term, to benefit PCs becoming strong enough to do local LLMs. Which makes this play make even more sense. Though, I've been saying for a while that the local AI inflectiom point is the death knell for these fron…
If people can get opus4.6/gpt5.5-like models locally, labs could raise their prices and sell token speed, better reasoning, mobile-focused improvements, you name it.
Not all consumers are power users and many will be happy to pay for flexibility.
Apple is actually interesting. They are one of the few companies with a chip / PC play with real power AND basically no play I'm the hyperscalar market. That means they're actually incentivized at least short term, to benefit PCs becoming strong enough to do local LLMs. Which makes this play make even more sense. Though, I've been saying for a while that the local AI inflectiom point is the death knell for these fron…
Earlier quoted context omitted.
> Though, I've been saying for a while that the local AI inflectiom point is the death knell for these frontier labs. "Death knell" is a touch hyperbolic. Hardware that can only run quantized models that take up GBs in VRAM falls short of even an A100 (by almost an order of magnitude[0]), which in turn falls short of what an 8xH100 cluster can do (also by another order of magnitude[0]). I'm an avid believer in local…
The big question for local LLMs is whether there is a 100 tok/s model which requires less than 16 GB of memory and is competitive on most tasks with the cloud models. There is some signal that this is possible through both hardware innovation and training/data improvements. Cloud models have their own constraints - I can’t have opus4.8 spend 4 hours on a deep research question I had in the shower without spending mon…
Benchmarks maybe? Real world, no.
You just need the context otherwise. There's no way around it.
Apple is actually interesting. They are one of the few companies with a chip / PC play with real power AND basically no play I'm the hyperscalar market. That means they're actually incentivized at least short term, to benefit PCs becoming strong enough to do local LLMs. Which makes this play make even more sense. Though, I've been saying for a while that the local AI inflectiom point is the death knell for these fron…
They do stand in front of a great opportunity that would also benefit consumers, which seems rare in the llm era. If people can get opus4.6/gpt5.5-like models locally, labs could raise their prices and sell token speed, better reasoning, mobile-focused improvements, you name it. Not all consumers are power users and many will be happy to pay for flexibility.