Mark Zuckerberg indicates Meta is spending billions of dollars on Nvidia AI chips

Submitted ⁨⁨3⁩ ⁨months⁩ ago⁩ by ⁨31337@sh.itjust.works⁩ to ⁨technology@lemmy.world⁩

https://www.cnbc.com/2024/01/18/mark-zuckerberg-indicates-meta-is-spending-billions-on-nvidia-ai-chips.html

Summary: Meta, led by CEO Mark Zuckerberg, is investing billions in Nvidia’s H100 graphics cards to build a massive compute infrastructure for AI research and projects. By end of 2024, Meta aims to have 350,000 of these GPUs, with total expenditures potentially reaching $9 billion. This move is part of Meta’s focus on developing artificial general intelligence (AGI), competing with firms like OpenAI and Google’s DeepMind. The company’s AI and computing investments are a key part of its 2024 budget, emphasizing AI as their largest investment area.

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tonytins@pawb.social ⁨3⁩ ⁨months⁩ ago
The real winners are the chipmakers.

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- whodatdair@lemm.ee ⁨3⁩ ⁨months⁩ ago
  Gold rush you say?
  
  Shovels for sale!
  
  Get your shovels here! Can’t strike it rich without a shovel!
  
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- FaceDeer@kbin.social ⁨3⁩ ⁨months⁩ ago
  I feel like a pretty big winner too. Meta has been quite generous with releasing AI-related code and models under open licenses, I wouldn't be running LLMs locally on my computer without the stuff they've been putting out. And I didn't have to pay a penny to them for it.
  
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  - Unforeseen@sh.itjust.works ⁨3⁩ ⁨months⁩ ago
    Subsized by boomers everywhere looking at ads on Facebook lol. Same with the Quest gear and VR development
    
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- fluxion@lemmy.world ⁨3⁩ ⁨months⁩ ago
  Was wondering why my stock was up. AI already improving my quality of life.
  
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simple@lemm.ee ⁨3⁩ ⁨months⁩ ago
Who isn’t at this point? Feels like every player in AI is buying thousands of Nvidia enterprise cards.

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- 31337@sh.itjust.works ⁨3⁩ ⁨months⁩ ago
  The equivalent of 600k H100s seems pretty extreme though. IDK how many OpenAI has access to, but it’s estimated they “only” used 25k to train GPT4. OpenAI has, in the past, claimed the diminishing returns on just scaling their model past GPT4s size probably isn’t worth it. So, maybe Meta is planning on experimenting with new ANN architectures, or planning on mass deployment of models?
  
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  - qupada@kbin.social ⁨3⁩ ⁨months⁩ ago
    The estimated training time for GPT-4 is 90 days though.
    
    Assuming you could scale that linearly with the amount of hardware, you'd get it down to about 3.5 days. From four times a year to twice a week.
    
    If you're scrambling to get ahead of the competition, being able to iterate that quickly could very much be worth the money.
    
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  - ExLisper@linux.community ⁨3⁩ ⁨months⁩ ago
    Or they just have too much money.
    
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  - NotMyOldRedditName@lemmy.world ⁨3⁩ ⁨months⁩ ago
    Would that be diminishing returns on quality, or training speed?
    
    If I could tweak a model and test it in an hour vs 4 hours, that could really speed up development time?
    
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  - TropicalDingdong@lemmy.world ⁨3⁩ ⁨months⁩ ago
    Might be a bit of a tell that they think they have something.
    
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lurch@sh.itjust.works ⁨3⁩ ⁨months⁩ ago
well Zuck has a lot of users he has to create bullshit for to keep them emotionally engaged and distracted

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the_q@lemmy.world ⁨3⁩ ⁨months⁩ ago
Jensen’s gonna buy so many new leather jackets.

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- qupada@kbin.social ⁨3⁩ ⁨months⁩ ago
  And spatulas. Don't forget the spatulas.
  
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  - massive_bereavement@kbin.social ⁨3⁩ ⁨months⁩ ago
    Could just buy Spatula City.
    
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Deceptichum@kbin.social ⁨3⁩ ⁨months⁩ ago
I really hope they fail hard and end up putting these devices on the consumer second hand market because the v100's while now affordable and flooding the market are too out of date.

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- FaceDeer@kbin.social ⁨3⁩ ⁨months⁩ ago
  Meta is the source of most of the open source LLM AI scene. They're contributing tons to the field and I wish them well at it.
  
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  - TropicalDingdong@lemmy.world ⁨3⁩ ⁨months⁩ ago
    Only other game in town really.
    
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werefreeatlast@lemmy.world ⁨3⁩ ⁨months⁩ ago
This is great! I thought there would be a chips LED recession. Sorry homeless people but you’re gonna have to wait another generation to try and get online to maybe buy a house someday far far away… and also some day far far away if you get my drift.

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elgordio@kbin.social ⁨3⁩ ⁨months⁩ ago

total expenditures potentially reaching $9 billion

I imagine they negotiated quite the discount in that.

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- Ghostalmedia@lemmy.world ⁨3⁩ ⁨months⁩ ago
  They signed up for spam email so they could get a coupon code.
  
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- DdCno1@kbin.social ⁨3⁩ ⁨months⁩ ago
  Agreed. There's volume discount, and then there is "Facebook data center with an energy consumption of a small country volume discount".
  
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chemicalwonka@discuss.tchncs.de ⁨3⁩ ⁨months⁩ ago
He needs a good AI bot to teach him to be “more human”

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Boozilla@sh.itjust.works ⁨3⁩ ⁨months⁩ ago
Just like the Metaverse…this won’t have legs.

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Rapidcreek@lemmy.world ⁨3⁩ ⁨months⁩ ago
I’m sure that everybody has some, but to spend billions seems a little premature.

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- FaceDeer@kbin.social ⁨3⁩ ⁨months⁩ ago
  Six months from now: "damn, we're way behind Meta on AI. We should have spent billions six months ago, it's going to cost way more to catch up."
  
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  - Rapidcreek@lemmy.world ⁨3⁩ ⁨months⁩ ago
    Chips evolve. By the time a billion dollar contract is fulfilled, they are two iterations behind.
    
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Wanderer@lemm.ee ⁨3⁩ ⁨months⁩ ago
Anyone got a graph of ai spending over time globally?

I’m starting to feel more confident about AGI coming soon (relatively soon).

Knowing absoultely nothing about it though it seems like it needs to be more efficient? What’s the likelihood rather than increasing the bulk power of these systems that there is a breakthrough that allows more from less?

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- 31337@sh.itjust.works ⁨3⁩ ⁨months⁩ ago
  Spending is definitely looks exponential at the moment: Image
  
  Most breakthroughs have historically been made by university researchers, then put into use by corporations. Arguably, including most of the latest developments,. But university researchers were never going to get access to the $100 million in compute time to train something like GPT-4, lol.
  
  The human brain has 100 trillion connections. GPT-4 has 1.76 trillion parameters (which are analogous to connections). It took 25k GPUs to train, so in theory, I guess it could be possible to train a human-like intelligence using 1.4 million GPUs. Transformers (the T in GPT) are not like human brains though. They “learn” once, then do not learn or add “memories” while they’re being used. They can’t really do things like planning either. There are algorithms for “lifelong learning” and planning, but I don’t think they scale to such large models, datasets, or real-world environments. I think there needs to be a lot theoretical breakthroughs to make AGI possible, and I’m not sure if more money will help that much. I suppose AGI could be achieved by trial and error (i.e. trying ideas and testing if they work without mathematically proving if or how well they’d work) instead of rigorous theoretical work.
  
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