lemmylommy@lemmy.world
on 21 Dec 2023 20:05
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Well, if that commenter had more than just a vague idea of caching and/or swapping, they would know that the right algorithm can make or break performance.
That paper is not “we invented caching”, but “this is how we make some certain models work well despite constraints imposed by RAM and flash storage.”
It’s a worthy job for an engineer or researcher. Not quite as innovative as the invention of the wheel, but still enough to write a paper on (and read it, if you can manage to understand it).
guitarsarereal@sh.itjust.works
on 21 Dec 2023 17:55
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Everyone likes to trash machine learning because the power requirements are high, but what they don’t realize is that we’re in the very first days of this technology (well, first couple decades of the technology being around, first few years of it being advanced enough to have anything to show off). Every technology that got bundled together into your phone was equally as useless when it was first invented. Honestly, compared to the development of most other technologies I’ve looked at, the pace of development in AI has been shocking.
Literally once a week, I see some news story about AI researchers delivering an order of magnitude speedup in some aspect of AI inference. The technique described here apparently allows for a 20x speedup on GPU’s.
cybersandwich@lemmy.world
on 21 Dec 2023 18:03
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Whispercpp works off the ML cores on the m series chips. It’s faster than my 1080ti that I have in a server doing the same things–by orders of magnitude. And it sips power.
Purpose built chips can be super powerful for their specific purposes.
fruitycoder@sh.itjust.works
on 22 Dec 2023 08:23
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Tbh I’m more excited to see someone do use webnn, webgpu and petals together. Building smaller tighter models is good too.
threaded - newest
This comment sums it up pretty nicely:
Apple being “innovative” my ass, lmao
Cache u inside
How bow dat?
The easiest way to tell that something's not really innovative is if the person describing it uses the word innovative.
Can you give an example of something that actually was innovative, that no-one called innovative?
The spoked wheel.
Interestingly, it looks as if nothing was really called innovative before 1960, with usage peaking in 2000, and its now in decline
https://www.etymonline.com/word/innovative
Peaking in 2000 seems odd - I see or hear that word daily and that definitely wasn't the case back in 2000. Interesting!
Corndogs
Well, if that commenter had more than just a vague idea of caching and/or swapping, they would know that the right algorithm can make or break performance.
That paper is not “we invented caching”, but “this is how we make some certain models work well despite constraints imposed by RAM and flash storage.”
It’s a worthy job for an engineer or researcher. Not quite as innovative as the invention of the wheel, but still enough to write a paper on (and read it, if you can manage to understand it).
35 upvotes in the technology community…man you guys really are just all knee-jerk reactionaries and it really knowledgeable tech at all. git gud
Make Siri Great For Once
Huhum…?
Still working on that…
I’m sorry, try again later.
You’re triggering me lol
Found the following websites about you’re triggering me lol.
Sigh! [unzips] Go ahead…
.
Why the hell do we want to encourage people running MLMS on our phones?!! I don’t want to be part of some stupid pyramid scheme nonsense.
Haha
dōTERRA Phone
HerbaLife Galaxy S30
😭
Here, you dropped this: /s
/s is for weaklings.
HEAR ME ROAR.
Siri could suck an order of magnetize less and work offline, for starters
LLM≠MLM
youtu.be/8N_tupPBtWQ?si=bDNpaip3U5tDk0h5
Everyone likes to trash machine learning because the power requirements are high, but what they don’t realize is that we’re in the very first days of this technology (well, first couple decades of the technology being around, first few years of it being advanced enough to have anything to show off). Every technology that got bundled together into your phone was equally as useless when it was first invented. Honestly, compared to the development of most other technologies I’ve looked at, the pace of development in AI has been shocking.
Literally once a week, I see some news story about AI researchers delivering an order of magnitude speedup in some aspect of AI inference. The technique described here apparently allows for a 20x speedup on GPU’s.
Whispercpp works off the ML cores on the m series chips. It’s faster than my 1080ti that I have in a server doing the same things–by orders of magnitude. And it sips power.
Purpose built chips can be super powerful for their specific purposes.
Tbh I’m more excited to see someone do use webnn, webgpu and petals together. Building smaller tighter models is good too.
I don’t understand the innovation, I already run LLMs and stable diffusion on a laptop from 2011.
I have no doubt it could be run on my Android phone.