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I get the impression they want AI marketing points but don’t actually want people to use local AI on their products. I have no idea why. They could be so successful if they leaned into local AI.
But apple have to own everything they do so they’re in a bind
I get the impression they want AI marketing points but don’t actually want people to use local AI on their products. I have no idea why. They could be so successful if they leaned into local AI.
I also don't think they do "technology". For example, containers have been around for a long time, and apple didn't show up. (I know they have some support now). Imagine an apple-native docker/podman doing something like FROM macos:10.12
I was actually surprised when they did their own chips. I figure it was about control.
Apple was also allegedly "caught off guard" by the Macbook Neo demand. I don't really see how they couldn't see the Local AI demand or demand for a cheaper Macbooks. This just reads like marketing imo.
this article's also about enterprise demand specifically. That's a bit surprising to me as well frankly. I'd have thought the primary market for mac studios would be hobbyists/enthusiasts with a bunch of disposable income who are willing to pay 18k for a 512 gb machine to run glm 3.5 flash or 9k to run deepseek v4 flash locally. It's competing with a $200/mo subscription or renting server gpu time for open source models during a memory shortage - and idk if it's going to be powerful enough to train or fine tune so it's really just inference. seems reasonable to be surprised
Most of those people will be dissapointed when they experience Q4 variants of those models getting stuck in loops. I would wait till the ram crisis is over to fetch a future 64gb ram gpu to run Q8 models. Cloud inference until than.
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What year are you talking about? When I was in grad school, around 2007, Nvidia was aggressively marketing GPUs for high performance computing. They would go to campuses, talk to professors, etc. Yes, the whole Deep Learning thing was luck, but as with most lucky things, they ensured they were positioned to capitalize on it.
Probably cerca 2014 as that's when AlexNet was released, demonstrating that neural networks could beat traditional ML models at image recognition tasks. I recall the researchers used Cuda to optimize their training setup. AlexNet kicked off a new wave of research around neural networks by demonstrating they could be scaled well and trained on GPUs.
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A new base model mac mini is $900. That is 45 month of Gemini. Gemini 4.7 Flash will give better OCR results that Qwen or GLM w/ 10GB.
That doesn't help with the not wanting to send confidential information to a cloud though. No amount of cost savings can negate that.
Neither openai or xai. Anthropic maybe but not likely. Mistral is the most likely one because they're under the EU laws but I think they focus more on commercial these days.
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This is such a tired argument and it seems to be parroted every single time someone talks about local models on hacker news. Yes, of course the most economical path is to hand over all your data and become fully dependent on a cloud provider who is already operating as scale, hoping that they won't change/remove models, hamstring capabilities, or raise prices. If this were a thread about hosting your own email or blo…
I don’t understand the willingness to give up privacy so easily, particularly if you are developing something that you plan to monetize somewhere down the road. I’m pretty sure that all of those disclaimers that all the AI model makers have for you to sign off on to say that they’re not responsible for anything that might go wrong if your work gets copied accidentally and used someplace else wink wink? You know the l…
But no way whatsoever I'm uploading my personal files, photos, emails, chats into cloud AI. No way.