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Apple Intelligence summary botches a headline, causing jitters in BBC newsroom
(www.theregister.com)
this post was submitted on 17 Dec 2024
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Emphasis on the first L in LLM. Apple's model is specifically designed to be small to work on phones with 8 gigs of ram (the requirement to run this)
The price gouging for RAM was only ever on computers. With phones you got what got, and you couldn't pay for more.
What do you mean couldn't pay for more? There are plenty of sub-$200 android phones with 8GB of RAM, and 12-16GB are fairly standard on flagships these days. Asus ROG Phone 6 is rather old and already came with 16GB what, three years ago?
It is definitely doable, there only needs to be willingness. Apple is definitely skimping here.
Yeah... and it kinda sucks because it's small.
If Apple shipped with 16GB/24GB like some Android phones did well before the iPhone 16, it would be far more useful. 16-24GB (aka 14B-32B class models) are the current threshold where quantized LLMs really start to feel 'smart,' and they could've continue trained a great Apache 2.0 model instead of a tiny, meager one from scratch.
I don’t know how much RAM is in my iPhone 14 Pro, but I’ve never thought ooh this is slow I need more RAM.
Perhaps, it’ll be an issue with this stupid Apple Intelligence, but I don’t care about using that on my next upgrade cycle.
My old Razer Phone 2 (circa 2019) shipped with 8GB RAM, and that (and the 120hz display) made it feel lighting fast until I replaced it last week, and only because the microphone got gunked up with dust.
Your iPhone 14 Pro has 6GB of RAM. Its a great phone (I just got a 16 plus on a deal), but that will significantly shorten its longevity.
I wonder how much more efficient the RAM can be when the manufacturer makes the software and the hardware? It has to help right, I don’t know what a 16 Pro feels like compared to this, but doubt I would notice.
Your OS uses it efficiently, but fundamentally it also limits what app developers can do. They have to make apps with 2-6GB in mind.
Not everything needs a lot of RAM, but LLMs are absolutely an edge case where "more is better, and there's no way around it," and they aren't the only one.