421–430 of 668 posts
Framing it in gigawatts is very interesting given the controversy about skyrocketing electric prices for residential and small business users as a result of datacenters over the past three years, primarily driven by AI growth. If, as another commenter notes, this 10GW is how much Chicago and NYC use combined , then we need to have a serious discussion about where this power is going to come from given the dismal stat…
Water is a critical resource in dwindling supplies in many water-stressed regions. These data centers have been known to suck up water supplies during active droughts. Is there anyone left at the EPA that gets a say in how we manage water for projects like this?
Where is this water meme coming from? Surely the water is just pumped around, not actually used up?
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I'm pretty average, living in a small home, and my electric bill is already >$500/mo in the summer, and that's with the A/C set at 76F during the day.
Where do you live? How old is your house? 500 is insane.
Framing it in gigawatts is very interesting given the controversy about skyrocketing electric prices for residential and small business users as a result of datacenters over the past three years, primarily driven by AI growth. If, as another commenter notes, this 10GW is how much Chicago and NYC use combined , then we need to have a serious discussion about where this power is going to come from given the dismal stat…
I work in the datacenter space. The power consumption of a data center is the "canonical" way to describe their size. Almost every component in a datacenter is upgradeable—in fact, the compute itself only has a lifespan of ~5 years—but the power requirements are basically locked-in. A 200MW data center will always be a 200MW data center, even though the flops it computes will increase. The fact that we use this unit…
Folks old enough to have been around in 2000 have seen this movie before. If this was such a great business, money would be coming from outside and Nvidia would be using its profits to scale production. But they know it's not and once the bubble pops, they profit margin evaporates in months. So they keep the ball rolling - this is pretty much equivalent to buying the cards from ... themselves.
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OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. I do buy that they are extremely over-valued if they have to slow down on model training. For cloud providers, the analysis is a bit more complex; presumably if training demand craters then the existing inference demand would be met at a lower price, and maybe you’d see some consolidation as margins…
> OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. But OpenAI can't stop training their next generation models. OpenAI already spends over 50% of their revenue on inference cost [1] with some vendors spending over 100% of their revenue on inference. The real cash cow for them is in the business segment. The problem here is models are rapidly cloned…
Zuckerberg said in an interview last week he doesn't mind spending $100B on AI, because not investing carries more risk.
onlyrealcuzzo wrote: > Google is pretty useful. It uses 15 TWh per year. 15TWh per year is about 1.7GW. Assuming the above figures, that means OpenAI and Nvidia new plan will consume about 5.8 Googles worth of power, by itself. At that scale, there's a huge opportunity for ultra-low-power AI compute chips (compared with current GPUs), and right now there are several very promising technology pathways to it.
This one datacenter should be able to perform a 51% attack on any of the big cryptocurrencies with that much compute. An interesting hedge in case the AI bubble pops.
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Safely in "millions of devices." The exact number depends on assumptions you make regarding all the supporting stuff, because typically the accelerators consume only a fraction of total power requirement. Even so, millions.
"GPUs per user" would be an interesting metric. (Quick, inaccurate googling) says there will be "well over 1 million GPUs" by end of the year. With ~800 million users, that's 1 NVIDIA GPU per 800 people. If you estimate people are actively using ChatGPT 5% of the day (1.2 hours a day), you could say there's 1 GPU per 40 people in active use. Assuming consistent and even usage patterns. That back of the envelope math…
Waiting patiently for the Ed Zitron article on this...
He single-handedly cost people more than anyone with his bearish takes lol
I say this as someone who has been holding NVDA stock since 2016 and can cash out for a large sum of money. To me its all theoretical money until I actually sell. I don't factor it into financial planning.
You don't see me being a cheerleader for NVDA. Even though I stand to gain a lot. I will still tell you that the current price is way too high and Jensen Huang has gotten high off his own supply and "celebrity status".
After all, we all can't buy NVDA stock and get rich off it. Is it truly possible for all 30,000+ NVDA employees to become multi-millionaires overnight? That's not how capitalism works.
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> OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. But OpenAI can't stop training their next generation models. OpenAI already spends over 50% of their revenue on inference cost [1] with some vendors spending over 100% of their revenue on inference. The real cash cow for them is in the business segment. The problem here is models are rapidly cloned…
That's why we are seeing these insane numbers. The competition is "do or die" right now. Zuckerberg said in an interview last week he doesn't mind spending $100B on AI, because not investing carries more risk.
To date, no evidence of either even exists. See Zuckerbergs recent live demo of Facebooks Ray Bans technology, for example.