3 ms·

To back parent's point, students (including moddle/high school) have roughly the same requirements and can get by with iPads or chromebooks, sometimes with 4Gb(

To back parent's point, students (including moddle/high school) have roughly the same requirements and can get by with iPads or chromebooks, sometimes with 4Gb(!) of RAM. So I'd agree there's some non negligible amount of users that will be fine with the absolute most basic functioning computer. The question could be why that user base would buy a Mac when they could get something way more cheaper and probably better adapted to their needs ? My answer would be they don't need to care about the money or utterly like macs, and it's their choice. It's still surprising Apple caters to those users though.

smcleod 3y ago
AI is here now and here to stay. By locking students into RAM suited for 2008~ era machines you’re locking them out of any local AI / LLM tooling and to the current apps they can (just) run today. RAM is super cheap - we can’t normalise companies hamstringing devices at almost no cost savings just to lock people into future upgrades.
- makeitdouble 3y ago
  The same thing was told about touch input, and macs still don't have it. People who care about AI are getting Windows machines for now. Perhaps in 10 years computer usage would wildly evolve (I hope so), but these machines are also not speced to last 10 years either.
- washadjeffmad 3y ago
  Agreed, but better-than-middling small model local LLM inference on the M-series has largely been a happy accident that Apple have been silently capitalizing on without bothering to correct. They were first in mobile, not the best. Generative performance and training are poor to nonexistent, Core ML is incomplete and underdeveloped, memory bandwidth was decreased on the M3 Pros, and eGPUs don't work with Apple Silicon - and what, normalizing 16GB of memory will encourage Apple to stop segmenting when they already have so little to offer? They're paygating /inference/ performance beyond their Pro chips. That's their official stance. Even Intel wasn't prepared for the Cambrian explosion of AGI, and their libraries and APIs have been taught in higher ed for the past decade. The good news is that Apple are not the best or only consumer option and will likely be out-classed by AMD and Intel in the next 18 months. The price of any new Mac for inference today would be better spent renting GPU time.

smcleod 3y ago

AI is here now and here to stay. By locking students into RAM suited for 2008~ era machines you’re locking them out of any local AI / LLM tooling and to the current apps they can (just) run today. RAM is super cheap - we can’t normalise companies hamstringing devices at almost no cost savings just to lock people into future upgrades.

makeitdouble 3y ago
The same thing was told about touch input, and macs still don't have it. People who care about AI are getting Windows machines for now. Perhaps in 10 years computer usage would wildly evolve (I hope so), but these machines are also not speced to last 10 years either.
washadjeffmad 3y ago
Agreed, but better-than-middling small model local LLM inference on the M-series has largely been a happy accident that Apple have been silently capitalizing on without bothering to correct. They were first in mobile, not the best. Generative performance and training are poor to nonexistent, Core ML is incomplete and underdeveloped, memory bandwidth was decreased on the M3 Pros, and eGPUs don't work with Apple Silicon - and what, normalizing 16GB of memory will encourage Apple to stop segmenting when they already have so little to offer? They're paygating /inference/ performance beyond their Pro chips. That's their official stance. Even Intel wasn't prepared for the Cambrian explosion of AGI, and their libraries and APIs have been taught in higher ed for the past decade. The good news is that Apple are not the best or only consumer option and will likely be out-classed by AMD and Intel in the next 18 months. The price of any new Mac for inference today would be better spent renting GPU time.