- cross-posted to:
- climate@slrpnk.net
- cross-posted to:
- climate@slrpnk.net
Writing a 100-word email using ChatGPT (GPT-4, latest model) consumes 1 x 500ml bottle of water It uses 140Wh of energy, enough for 7 full charges of an iPhone Pro Max
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🥵🥵🥵🔥🔥🔥💦💦💦
Can we PLEASE shut that shit down? We were doing just fine without it.
Mark my words: generative “AI” is the tech bubble of all tech bubbles.
It’s an infinite supply of “content” in a world of finite demand. While fast, it is incredibly inefficient at creating anything, often including things with dubious quality at best. And finally, there seems to be very little consumer interest in paid-for, commercial generative AI services. A niche group of people are happy to use generative AI while it’s available for free, but once companies start charging for access to services and datasets, the number of people who are interested in paying for it will obviously be significantly smaller.
Last I checked there was more than a TRILLION dollars of investment into generative AI across the US economy, with practically zero evidence of genuinely profitable business models that could ever lead to any return on investment. The entire thing is a giant money pit, and I don’t see any way in which someone doesn’t get left holding the $1,000,000,000,000 generative AI bag.
Don’t worry, we’ll bail them out once the bubble bursts.
140Wh seems off.
It’s possible to run an LLM on a moderately-powered gaming PC (even a Steam Deck).
Those consume power in the range of a few hundred watts and they can generate replies in a seconds, or maybe a minute or so. Power use throttles down when not actually working.
That means a home pc could generate dozens of email-sized texts an hour using a few hundred watt-hours.
I think that the article is missing some factor, such as how many parallel users the racks they’re discussing can support.
The study that suggests 10-50 interactions with ChatGPT evaporates a whole bottle of water, doesn’t account for the fact that cooling systems are enclosed…
…and that “study” is based on a bunch of assumptions, which include evaporation from local power plants, as well as the entire buildings GPT’s servers are located in. It does this as if one user is served at a time, and the organizations involved (such as microsoft) do nothing BUT serve one use at a time. So the “study” (which isn’t peer reviewed and never got published) pretends those buildings don’t also serve bing, or windows, or all the other functions microsoft is involved with. It instead assumes whole buildings at microsoft are dedicated to serving just one user of ChatGPT at a time.
It also includes the manufacture of all the serve and graphics cards equipment, even though the former was used before ChatGPT, and will be used for other things as well… and the latter is only used in training.
You can check the study out yourself here:
http://arxiv.org/pdf/2304.03271
It’s completely junk. Worthless. Even uses a click bait title, and keeps talking about “the secret water foot print” as if it’s uncovering some conspiracy. It’s bunk science.
P.S It also doesn’t seem to understand that the bulk of GPT’s training was a one time cost, paid in 2021, with one smaller update in 2023.
Datacenter LLM tranches are 7-8 H100s per user at full load which is around 4 kW per second.
Multiply that by generation time and you get your energy used. Say it takes 62 seconds to write an essay (a highly conservative figure).
That’s 68.8 Wh, so you’re right.
Source: I’m an AI enthusiast
Well that’s of the same order of magnitude as the quoted figure. I was suggesting that it sounded vastly larger than it should be.
They’re probably factoring in cooling costs and a bunch of other overhead, I dunno
You are conveniently ignoring model size here…
Which is a primary impact on power consumption.
And any other processing and augmentation being performed. System prompts and other things that are bloating the token size …etc never mind the fact that you’re getting a response almost immediately for something that an at home GPU cluster (not casual PC) would struggle with for many minutes, this isn’t always a linear scale for power consumption.
You are also ignoring the realities of a data center. Where the device power usage isn’t the only power consumption of the location, cooling must be taken into consideration as well. Redundant power switching also comes with a percentage loss in transmission efficiency which adds to power consumption and heat dispersion requirements.
It’s true, I don’t know how large the models are that are being accessed in data centers. Although if the article’s estimate is correct, it’s sad that such excessively-demanding models are always being used for use-cases that could often be handled with much lower power usage.
This seems a big waste of energy if that 140Wh (504,000 joules) number is correct. That amount of energy is about 2,000 times what it would take to do a very similar thing on a home PC.
Writing a 100 word email with a 7B model would take my PC about 5 seconds, times an increased power use of 50 watts, so 250 joules.
I get that they might be using a much larger model, but the e-mail is not going to be 2,000 times better.
That’s what I always thought when reading this and other articles about the estimated power consumption of GPT-4. Run a decent 7B LLM on consumer hardware like the steam deck and you got your e-mail in a minute with the fans barely spinning up.
Then I read that GPT-4 is supposedly a 1760B model. (https://en.m.wikipedia.org/wiki/GPT-4#Background) I don’t know how energy usage would scale with model size exactly, but I’d consider it plausible that we are talking orders of magnitude above the typical local LLM.
considering that the email by the local LLM will be good enough 99% of the time, GPT may just be horribly inefficient, in order to score higher in some synthetic benchmarks?
Computational demands scale aggressively with model size.
And if you want a response back in a reasonable amount of time you’re burning a ton of power to do so. These models are not fast at all.
Thanks for confirming my suspicion.
So, the whole debate about “environmental impact of AI” is not about generative AI as such at all. Really comes down to people using disproportionally large models for simple tasks that could be done just as well by smaller ones, run locally. Or worse yet, asking a behemoth model like GPT-4 about something that could and should have been a simple search engine query, which I (subjectively) feel has become a trend in everyday tech usage…
I’m sure I’m missing out, but i have no interest in using chatbots and other LLMs etc. It floors me to see how much attention they get though, how much resources are being dumped into their development and use. Nuclear plants being reopened for the sake of AI?!!
I also assume there’s a lot of things they’re capable of that could be huge for science, and there’s likely lots of big things happening behind closed doors that we’re yet to see in the coming years. I know it’s not all just chatbots.
The way this article strikes me though, is that it’s pretty much just wasting resources for parlor-game level output. I don’t know if i like the idea of people giving up their ability to write a basic letter or essay, not that my opinion on the matter is gonna change anything obviously 😅
Nuclear plants being reopened for the sake of AI?!!
Do you have ANY evidence this is happening?
Think of it like this: rich people accumulate more wealth by paying fewer people to accomplish more work faster, so it’s worth burning through the worlds resources at breakneck speed to help the richies out, right?
Hah! Haha! Hahahaahah! Ties well with this one news article that I glimpsed that claims that by 2030 the need for fresh water will be 140% of the world’s freshwater reserves. Infinite growth forever!
Time to buy stock in water lol
So, Nestlé stocks?
New admin will do its part by discouraging pregnancy and encouraging people to die sooner.
Why does the article make it sound like cooling a data center results in constant water loss? Is this not a closed loop system?
I’m imagining a giant reservoir heat sink that runs throughout a complex to pull heat out of the surrounding environment where some liquid evaporates and needs to be replenished. But first of all we have more efficient liquid coolants, and second that would be a very lazy solution.
I wonder if they’ve considered geothermal for new data centers. You can run a geothermal loop in reverse and use the earth as a giant heat sink. It’s not water in the loop, it’s refrigerant, and it only needs to be replaced when you find the efficiency dropping, which can take decades.
It is a closed loop, but the paper treats it as if it’s an open loop, and counts all water use for the building, as well as all the water that went into creating any equipment used… and the water that escapes power plants in powering the buildings… it also includes any other buildings that might house related services. Here is the original “study” which is about what maths could be done given the above assumptions:
http://arxiv.org/pdf/2304.03271
In short, it has nothing to do with reality, and is more just an attempt at the authors to get their names out there (on bad science that the media is interested in publicizing for click bait reasons).
Evaporative coolers save a ton of energy compared to refrigerator cycle closed loop systems. Like a swamp cooler, the hot liquid that comes from cooling the server is exposed to the atmosphere and enough evaporates off to cool the liquid by a decent percentage, then it’s refrigerated before going back into the servers.
Data centre near me is using it and the fire service is used to be being called by people concerned the huge clouds of water vapor are smoke
You can run a geothermal loop in reverse and use the earth as a giant heat sink.
You need something to move the heat away, like water or air. Having something solid that just absorbs will reach its heat capacity pretty quick.
I don’t know, but given that ground-source heat pumps are one of the most efficient ways to heat a building, and this suggestion is just exactly that in reverse (pumping the heat into the ground instead of out of it), I’d imagine that it will not just “reach its heat capacity”. The heat would flow away just as it flows to a heat pump. If the entire earth reaches its heat capacity I think we’d have problems.
Deep Geothermal goes deeeeeepppp to where there is a heat source that is replenished.
Shallow geothermal pulls heat where there is no replenishment, and you have to run it in reverse (use it as an AC in the summer) to swap out the heat. You can’t only pull heat out for shallow geothermal. You may be able to for a time, but also remember that heating for a house is pretty small overall.
It’s not the entire earth that is the heat sink, it’s a relatively short distance from the pipe. We don’t get the massive heat from the molten core at the surface.
It highly depends on every data center, but it is very likely that they do use municipal water for cooling. Mainting a Reservoir is extremely expensive for the amount of thermal mass it requires, these things kick off HEAT.
I don’t know why they aren’t using reclaimed water from treatment plants. I don’t see why potable water is necessary as long as the substitute isn’t corrosive, but I might be missing something here.
You’d have to get the gray water in, and it’s more efficient to just continue treating it and using the municipal water system.
Yes, the vast majority are closed loop systems and the water isn’t really used up, like a lot of these headlines imply.
That’s not to say the energy being used can’t be put to better uses, though.
Not used up per se but sequestered. It’s water that nobody will ever get to drink or use for crops, etc.
The math on this doesn’t really check out. The USA uses 322 billion gallons of fresh water per day. A hyperscale datacenter uses only 5 million gallons per day.
There are about 1,000 hyperscale datacenters in the USA, so that comes out to 5 billion gallons of water every day.
That’s 1.5% of our annual freshwater usage, half of which is in closed loop systems and not going anywhere, and the other half being returned to the atmosphere where it will rain back down as fresh water again.
And of course, the water cycle doesn’t really care about national borders or annual evaporation rates so much, and there is about 1 quintillion gallons of liquid fresh water available worldwide, so its not like sequestering 5 million gallons really offsets the available freshwater needed for hydration and agriculture.
It could be used for other things like district heating at least.
Datacenters are usually not located where this would be useful. They’re placed where space and energy are cheap, because everything they do only needs Internet access. At most they’d heat the rest of the building for whatever office space there is.
Absolutely my favorite game ever
Blowing stuff up with Arty is never not funny for me
yeah, but it can do really cool things like “suggest a name for my project that does X”.
surely that game’s worth the candle, yes?
The real surprise for me is how little the battery of my iphone holds. Especially compared to my ev6 or what my heat pump guzzles daily. Crazy.
