"xAI entirely bypassed the grid and generated power onsite, using truck-mounted gas turbines and engines."
So they solved the power problem by consuming more fossil fuel. Got it.
How AI labs are solving the power problem
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Re: How AI labs are solving the power problem
#22I found Boom's pivot much less confusing after this article.
Boom’s pivot to trying to build turbines for data centers wasn’t surprising when data center deployments started using turbines. Either their CEO saw one of the headlines or their investors forwarded it over and it became their new talking point.
What is interesting is how many people saw the Boom announcement and came to believe that Boom was a pioneer of this idea. They’re actually a me-too that won’t have anything ready for a long time, if they can even pull it off at all.
Re: How AI labs are solving the power problem
#23Our kids are not going to be happy we spun up more CO2 generation for this.
their uploaded minds will enjoy the infinite AI slop though
Re: How AI labs are solving the power problem
#24> An AI cloud can generate revenue of $10-12 billion dollars per gigawatt, annually. Citation needed.
This is coming from a group that does analysis on the semiconductor and cloud industries and provided very expensive access to their models and info. They are the citation.
So I guess it’s not a bubble then since these companies are raking in the big revenues? Or maybe they are counting all those circular investments as revenues somehow?
Re: How AI labs are solving the power problem
#25What about renewables + battery storage? Does it take much longer to build? I can imagine getting a permit can take quite a long time, but what takes so long to set up solar panels and link them to batteries, without even having to connect them to the grid?
Reciprocating natural gas engines can be moved from [concrete] pad to pad and be up and running in under 24 hours. The portable turbines take longer but they’re still fast.
Acquiring enough solar panels and battery storage still takes a very long time by comparison.
Re: How AI labs are solving the power problem
#26Part of what bothers me with AI energy consumption isn't just how wasteful it might be from an ecological perspective, it's how brutally inefficient it is compared to the biological "state of the art" — 2000kcal = 8,368 kJ. 8,368 kJ / 86,400 s = 96.9 W. So the benchmark is achieving human-like intelligence on a 100W budget. I'd be very curious to see what can be achieved by AI targeting that power budget.
You can't compare a training run that produces a file which can be run forever after to a human day
Re: How AI labs are solving the power problem
#27> Eighteen months ago, Elon Musk shocked the datacenter industry by building a 100,000-GPU cluster in four months. Multiple innovations enabled this incredible achievement, but the energy strategy was the most impressive. xAI entirely bypassed the grid and generated power onsite, using truck-mounted gas turbines and engines.
Wow, "truck-mounted gas turbines"? Who else could have mastered such a futuristic tech in so short a time? Seriously, who wrote this? Grok? And let's ignore that this needless burning of fossil fuel is making life on Earth harder for everyone and everything else.
Re: How AI labs are solving the power problem
#28Power problem: solved Natural Gas supply problem: worsened Carbon in the atmosphere problem: worsened
The natural gas turbines used are relatively efficient as far as engines go. Having them on-site makes transmission losses basically negligible.
Nothing short of full solar connected to batteries produced without any difficult to mine elements will make some people happy, but as far as pollution and fuel consumption data centers aren’t really a global concern at the same level as things like transportation.
Re: How AI labs are solving the power problem
#29Part of what bothers me with AI energy consumption isn't just how wasteful it might be from an ecological perspective, it's how brutally inefficient it is compared to the biological "state of the art" — 2000kcal = 8,368 kJ. 8,368 kJ / 86,400 s = 96.9 W. So the benchmark is achieving human-like intelligence on a 100W budget. I'd be very curious to see what can be achieved by AI targeting that power budget.
Is it though? When I ask an LLM research questions, it often answers in 20 seconds what it would take me an entire afternoon to figure out with traditional research.
Similarly, I've had times where it wrote me scientific simulation code that would take me 2 days, in around a minute.
Obviously I'm cherry-picking the best examples, but I would guess that overall, the energy usage my LLM queries have required is vastly less than my own biological energy usage if I did the equivalent work on my own. Plus it's not just the energy to run my body -- it's the energy to house me, heat my home, transport my groceries, and so forth. People have way more energy needs than just the kilocalories that fuel them.
If you're using AI productively, I assume it's already much more energy-efficient than the energy footprint of a human for the same amount of work.
Re: How AI labs are solving the power problem
#30> Wärtsilä, historically a ship engine manufacturer, realized the same engines that power cruise ships can power large AI clusters. It has already signed 800MW of US datacenter contracts.
This seems like a big reach for me. Their largest engine (and it is absolutely massive) "only" produces 80MW of power. The Brayton cycle is unbeatable if you need to keep scaling power up to ridiculous levels.