Yes, public knowledge that they will have 600,000 H100 equivalents by the end of the year. However having that many GPUs is not the same as efficiently networking 100,000 into a single cluster capable of training a frontier model. In May they announced their dual 25k H100 clusters, but no other official announcements. The power requirements alone are a big hurdle. Elons 100K cluster had to resort to I think 12 massive portable gas generators to get enough power.
just curious,
why is it so hard to build a 100k gpu cluster, and how was xAi able to do so?
And why did people think that making a cluster bigger then 30k is impossible.?
Last question, how will elon make the 1million gpu cluster
It's all about profit margins. Meta ads is a literal money printer. There is way less margin in public cloud. If they were to pivot into that, they'd need to spend years generalizing as internal infra is incredibly Meta-specific. And, they'd need to take compute away from the giant clusters they're building...
Llama 3.1 feels like it came out just yesterday, damn this field is going at light speed.
Any conjecture as to when or where about Llama 4 might drop.
I'm really excited to see the story telling finetunes that will come out after...
Yes but bubble is about output, not input.
No one can say that the big tech aren’t buying loads of accelerators and training impressive models.
The question is, will that flow through into real economic benefit: more bread, more corn, more t-shirts, or real digital goods people will pay for, and will the amount people are willing to pay for them exceed the cost to train and run the models?
If it doesn’t, this IS a bubble. It’s just that the big techs are part of the “bag holders” this time. They’ll be stuck with a huge amount of AI and no way to turn it into $$.
No, they’re real goods, that people will pay for.
Most gen.AI agents I am seeing are just being “baked in” for free, or the cost is so high it’s not worth it.
Yes but that doesn’t matter. That’s like the peloton story x 100.
It’s so exciting!! Everyone wants a peloton! But will you really lock in customers who will pay monthly bill for four years to cover your costs? We know how that one turned out.
I think Antrhopic, with their sonnet 3.5 model, are charging what is as close as possible to the cost of inference. They’ve basically written off training and are just charging for production: like what US pharmaceutical manufacturers do in third world countries. Even then, the top complaint on Claude is usage limits.
People are fundamentally estimating how expensive this tech is. Yes, it can do the job of a junior financial analyst. But the FA is 55k a year and the AI is 280k a year in inference and god knows what in training (depends on adoption).
A product does not replace an existing product at the same quality until it is cheaper.
Where this is headed, is wage reduction for knowledge workers to stay below the AI line, followed by a huge surplus of GPU compute, followed by a very short (5 year) AI winter, then a refocus on what AI is great at: prediction.
The companies who have the capital to push through (Amazon, Google, Meta, Apple, Microsoft), and parallel industries to make products from generative AI (meta, and apple) will make bank. So many will be crushed.
And the standard blah blah blah exponential growth - if we expected compute to continue at Moores law, we wouldn’t be talking about building fusion power plants and using a real percentage of the earths energy on new data centres. All of NVidia hand wavy faster than Moores law conversation is them just reducing precision.
> I think Antrhopic, with their sonnet 3.5 model, are charging what is as close as possible to the cost of inference
Well I think they're serving it at 90% markup.
Gemini models and DeepSeek show that you can come pretty close with very cheap inference. Sonnet is a good model but it almost certainly doesn't cost more than, like, $5 to generate 1M tokens.
Keep in mind that we're one disruptive innovation away from the bubble popping. If someone figures out a super innovative way to get the same performance on drastically less compute (e.g. CPUs or a dedicated ASIC that becomes commodity), it's going to be a rough time for Nvidia stock.
Nvidia knows this, and that's why they're trying to lock in customers. But I do think it's inevitable, and it will first start with big tech developing their own chip. Heck, Google and Amazon already have their own in-house chips for both training and inference. Apple also uses Google's TPU to train its models and doesn’t buy Nvidia chips in bulk. Only Meta and Twitter seem like the ones that are buying a boatload of A100s to train AI. I'm pretty sure Meta is also planning, if not already working on, its own chip.
Which bubble are you popping ? Dramatically reducing the cost of training and inference will likely create more usages where it was not economically feasible.
That's not exactly correct - it would have to both reduce the amount of compute needed drastically, *and* not scale. Because otherwise, they would take the same compute and the training advantages and take their X% increase in efficiency. It seems pretty logarithmic in terms of efficiency, so if it's, say, 10% compute, they could train on the same effectiveness as 10x their current compute.
It would just generally be a boon, but for Nvidia to fall a really good competitor in hardware needs to be made that isn't relying on tsmc.
It could happen if the equivalent efficiency ended up quite a bit better on a different type of hardware entirely, true, but that's highly unlikely.
Meta was able to build their cluster cheap because NVidia dramatically increased production volume right when crypto crashed. They’re not secondhand, but they were discounted thanks to crypto
This was in 2022. Look at NVidias stock during that time. Meta announced a massive deal to buy a ton of GPUs. They just wrote off a bunch of money on their filings, they advertised 3D modeling and VR “omniverse” stuff.
https://www.sec.gov/Archives/edgar/data/1045810/000104581022000008/q4fy22pr.htm
https://www.nextplatform.com/2022/01/24/meta-buys-rather-than-builds-and-opens-its-massive-ai-supercomputer/
>This of, course, happened before the AI explosion that kicked off Nov 2022.
To your point, Meta purchased the GPU's then for reels. [Here's him talking about it with Dwarkesh Patel](https://youtu.be/bc6uFV9CJGg?t=296)
Nothing in the links in your previous comment related to crypto, and you'll have to be more explicit about what you are trying to get me to infer because I'm not seeing the connections.
I'm saying that' the crypto market performance was spuriously correlated with NVIDIA behaviour, not casually related
I don't think NVIDIA would have scaled manufacturing based on crypto because of the huge risk, and I see no evidence NVIDIA was upscaling manufacture of efficient crypto-friendly chips.
Crypto wasn’t just *market performance*, there were obvious GPUs actually involved. Nvidia made a lot of those GPUs, and since there were shortages, obviously they wanted to make *more*, to capture that demand. Unfortunately, the perfect storm was NVidia switching suppliers from 2021-2022 (Samsung to TSMC) meant they were the back of the queue for TSMC production- don’t forget the industry wide chip shortage at the time. This impact NVidias ability to quickly scale and meet demand, and explains why they missed it - they had to buy farther out and pre-commit more.
To connect it to Crypto directly.. They mostly built increased capacity for gamers, a core market at the time, who were spurned trying to buy a GPU and being outbid during shortages by crypto miners. But, fate would have it, that when crypto crashed everyone dumped used GPUs, and growth slowed tremendously in that market (gaming market). Then they delayed the Ada architecture because of the oversupply, and repriced products *down* at that time to compete with the used market.
Let me repeat that last part again: NVidia lowered the price of their silicon (coincidentally) at the same time as ChatGPT was announced.
Their entire history, through 2022, the portion of their revenue from gaming was much higher than today 2024. They’re a public company, this is easy to audit (say, from the SEC link I shared). Data center sales didn’t outpace gaming until 2023, post ChatGPT craze starting. To connect it to cheap gaming GPUs… they had gaming GPUs they couldn’t sell, even when discounted, their stock was down 50%, and they even delayed their next-gen chips due to low demand. They needed (1) a way to juice revenue numbers, and desperately, and (2) they needed something to do with their TSMC capacity they purchased during the height of the global chip shortages.
Then, Zuck comes around, and allocates >$1.xB in data center sales. Zuck needed some ML compute to improve recommendation algorithms after Apple turned off tracking (App tracking transparency), wiping out billions in metas revenue. Facebook was ~25% of their data center sales from that sec filing, and roughly 4% of their entire 2022 revenue - enough to go bring 2022 sales from a down year to a flat year. Nvidia stock was down a ton over 2022, as sales growth collapsed. They were growing >50%yoy for years, but then grew ~0% in 2022 (with Metas help). Meta basically pre-purchased a huge chunk of 2023 sales at NVidia’s weak moment.
You mentioned NVidia not making a “risky bet” on crypto… but their entire history has been trend chasing. If you look at Nvidia in 2022, it was all metaverse and VR. 2023 saw a hard pivot in their marketing to AI/LLMs.
https://www.statista.com/statistics/1120484/nvidia-quarterly-revenue-by-specialized-market/
Did this like up with one of metas big GPU purchases. I recall seeing zuck in an interview dating they were fortunate to have huge volumes of GPU setup(or ordered) which reduced lead time on them jumping into llama development. He said they were probably going to be used for metaverse, but that it was sort of a speculative purchase. Basically, he knew they would need a shit load of GPUs, but was entirely sure what for.
I guess it would make sense if crypto crash caused a price drop.
The AI boom came immediately after the crypto crash. ML needs a ton of GPU compute, and data centres full of GPUs were underutilised and relatively cheap due to low demand.
Current systems are using a lot of new GPUs because the demand has outstripped the available resources, but they're also still using a lot of mining compute that's hanging around.
Crypto wasn't just people with 50 GPUs in a basement. Some data centres went all in with thousands in professional configurations. Google and Meta aren't buying second hand GPUs on Facebook, but OpenAI were definitely using cheap GPU compute to train GPT2/3 when it was available.
You'll have to demonstrate the timeline in nVidia scaling manufacturing was unrelated to AI, because you're arguing they were scaling for crypto before crypto crashed... if that were the case, why not scale manufacturing earlier?
Why did they scale with AI optimised chips, and not crypto-optimized chips?
The scaling in manufacturing is also related to AI in another way via AI improving their manufacturing efficiency.
They scaled up for crypto, then crypto crashed which led to a brief period in 2022 where it looked like Nvidia had over extended themselves and was going to end up making too many GPUs. However things quickly shifted as AI took off and since then they've scaled up even more for AI, and have also shifted production towards AI specific products because TSMC can't scale fast enough for them.
The A100 was announced in 2020 though. And that article only mentions gaming demand, whereas crypto wants the efficiency of the 3060 which still seemed under supplied and at the time... if NVIDIA was scaling for crypto it would have scaled manufacturing of its most efficient products, not its most powerful.
It still reads like a spurious correlation to me. Tempting to assume causation but it doesn't seem sound in the details.
That's nonsense. Bitcoin stopped being profitable on GPU's in 2011, so like 99% of GPU mining was Ethereum. That did not stop because Ethereum crashed, it stopped because Ethereum moved to proof of stake.
Ethereum took a big dive in 2022, at the time it went PoS. As did most of the coins linked to it. That was about the time GPT3 was being trained.
There was suddenly a lot more datacentre GPU capacity available, meaning training models was cheaper, meaning GPT3 could be trained better for the same cost, meaning ChatGPT was really good when it came out (and worth sinking a lot of marketing into), meaning people took notice of it.
>Ethereum took a big dive in 2022, at the time it went PoS.
Yes but 2 years later it came back up. But GPU mining never returned because ETH was no longer minable and no other minable coins have grown as big as ETH since.
It did, but it doesn't really matter. Training LLMs isn't tied to crypto other than the fact they both used GPU compute and cheap GPU access at the right time helped LLMs to take off faster than they would have without it. The GPUs freed up by both the general dip across all crypto and the ETH PoS kick-started the LLM boom. After it got going there's been plenty of investment.
No "to be fair" about it. A country and a company are not comparable, just because they have a similar amount of money sloshing around.
May as well say a diamond ring is as good as a car.
Analogies are never perfect, but it’s valid to say that the resources and capital that Meta has allows it to do some things that some countries cannot.
Of course Meta can’t join the UN or start wars like a small country can.
The most obvious sign that AI is a bubble (or will be given current tech) is that the main source of improvements *is* to use the power input of entire countries.
If AI hypothetically goes far beyond where it is now, it won’t be through throwing more power and vram at it.
It will. Mark talked about that, Sam talked about that, Huang talked about that... We are using AI to have more powerful AI's(agents), and more agents to have yet more agents... We are limited by power.
They talked about it because they need people investing in that infrastructure, not because there won't or shouldn't be advancements in the actual techniques used to train models that could downscale the amount of raw power needed.
If machine learning techniques advance in a meaningful way in the next decade, then in twenty years we'll look back on these gigantic datacenters the way we look at "super computers" from the 70s today.
The GPT transformer model that revolutionized LLM training had nothing to do with using more electricity. It was a fundamental improvement of the training process using the same hardware.
Are you under the impression that computational linguists and machine learning researchers only spend their time sourcing more electricity and buying Nvidia GPUs to run the same training methods we have today? That would be ridiculous.
My claim was that they need investors to build more infrastructure. They want to build more infrastructure to power more GPUs to train more models right? Then they need money to do it. So they need investors. That’s just how that works. I don’t know what numbers you need when they all say that outright.
And yes we have needed less energy to do the same or more workload with computers, that’s one of the main improvements CPU engineers work on every day. See?
https://gamersnexus.net/megacharts/cpu-power#efficiency-chart
I’m just saying that the consumed power of the GPUs’ calculation can result in different outcomes, while I think that training an AI model is way better than mining the cryptos in terms of power consumption.
It makes certain tasks easier - not life easier for everyone. In fact I would argue this is only going to benefit large corporations and the wealthy investor class over any benefits to average people.
So many things giod and bad going on, I guess I wouldn’t mind living to see humanity building a Dyson sphere or something, powering some really beefy number crunchers to draw extremely detailed waifus… just kidding. :)
My point is that power input does not mean it’s not a bubble. We’ve seen similar power inputs to other tech projects that are bubbles.
In fact, there’s a similarity here. The cost per query in AI is a similar problem as the cost per block in blockchain based cryptos. The big difference I suppose is that the incentive for AI is to lower that cost, but for crypto is was a core feature.
Bottom line, I’m pointing out that a large power I put to the project doesn’t have anything to do with it being or not being a bubble.
With AI imposing such significant constraints on grid capacity, it’s surprising that more big tech companies don’t invest heavily in nuclear power to complement renewable energy sources. The current 20% efficiency of solar panels is indeed a limitation, and I hope we’ll see more emphasis on hybrid solutions like this in the future
The human brain runs on 20 watts. I'm not so sure intelligence will keep requiring the scale of power we are on with ai for the moment. Maybe, just something people should keep in mind.
Exactly. Everyone is talking about the Meta and xAI clusters right now. No one is talking about the massive GPU clusters the DoD is likely building right now. Keep in mind the US DoD can produce a few less tanks and jets in order to throw a billion dollars at something and not blink an eye. The Title 10 budgets are hamstrung by the POM cycle, but the black budgets often aren't. Can't wait to start hearing about what gets built in at a national scale...
It's true, they are limited by access to the grid and cooling. [One B200 server rack runs you half a megawatt.](https://docs.nvidia.com/https:/docs.nvidia.com/nvidia-dgx-superpod-data-center-best-practices-with-dgx-b200.pdf)
I was at a conference 6 months ago where a guy from Mets talked about how they had ordered a crapload (200k ?) of GPU for the whole Metaverse thing, Zuck ordered them to repurpose to AI when that path opened up. Apparently he had ordered way more than they needed to allow for growth, he was either extremely smart or lucky - tbh probably some of both
quantum computing is still a pretty nascient field, with the largest stable computers in the order of 1000’s of qubits, so it’s just not ready for city sized data center scale
I suppose it could be useful for new AI architectures that utilize scaled up quantum computers to be more efficient, but said architectures are also pretty exploratory since there aren’t any scaled up quantum computers to test scaling laws on them.
I think if you took some time to understand quantum computing you would realize that your comment comes from a fundamental misunderstanding of how it works.
our hardware is different. When 3d stacking will become a thing for processors, then they will use even less energy than our brain. All the processor are 2D as of today.
our hardware is different. When 3d stacking will become a thing for processors, then they will use even less energy than our brain. All the processor are 2D as of today.
Guys, we are living at the exponential curve. Things will EXPLODE insanely quickly. I'm not joking when I state that immortality might be achieved(Just look up who Bryan Johnson is and what he's doing)
The engineering team released in a blog post last year that they will have 600,000 by the end of this year.
Amdahl's law means that it doesn't mean they will necessarily be able to network and effectively utilize all that at once in a single cluster.
In fact llama 3.1 405B was pre-trained on a 16,000 H100 gpu cluster.
Yeah the article that showed the struggles they overcame for their 25,000 h100 GPU clusters was really interesting. Hopefully they release a new article with this new beast of a data center and what they had to do for efficient scaling with 100,000+ GPUs. At that number of gpus there has to be multiple gpus failing each day and I'm curious how they tackle that.
Mind linking that article? I, in turn, could recommend this one by SemiAnalysis from June, even the free part is very interesting: https://www.semianalysis.com/p/100000-h100-clusters-power-network
According to the llama paper they do some sort of automated restart from checkpoint. 400+ times in just 54 days. Just incredibly inefficient at the moment
I don't think restart counts scale linearly with size, but probably logarithmically. You might have 800 restarts, or 1200. A lot of investment goes to keeping that number as low as possible.
Nvidia, truth be told, ain't nearly the perfectionist they make themselves out to be. Even their premium, top-tier GPUs have flaws.
At this scale, reliability becomes as much of a deal as VRAM. Groq is cooperating with Meta, I suspect this may not be your commoner H100 that ends up in their 1M GPU cluster.
In short, kubernetes.
Also a fuckload of preflight testing, burn in, and preemptively killing anything that even starts to look like it's thinking about failing.
That plus continuous checkpointing and very fast restore mechanisms.
That's not even the fun part, the fun part is turning the damn thing on without bottlenecking *literally everything.*
600k is metas entire fleet, including Instagram and Facebook recommendations and reels inference.
If they wanted to use all of it I'm sure they could get some downtime on their services, but it's looking like they will cross 1,000,000 in 2025 anyway
I think the majority of that infra will be used for serving, but gradually Meta is designing and fabbing its own inference chips. Not to mention there are companies like Groq and Cerebras that are salivating at the mere opportunity to ship some of their inference chips to a company like Meta.
When those inference workloads get offloaded to dedicated hardware, there's gonna be a lot of GPUs sitting around just rarin' to get used for training some sort of ungodly scale AI algorithmns.
Not to mention the B100 and B200 blackwell chips haven't even shipped yet.
I wonder if Cerebras could even produce enough chips at the moment to satisfy more large customers? They already seems to have their hands full building multiple super computers and building out their own cloud service as well.
At what point does it make sense to made their own chip to train AI? Google and Apple is using Tensor chip to train AI instead of Nvidia GPU which should save them a whole lot of cost on energy
I was just at Pytorch Con, a lot is improving on the SW side as well to enable scaling past what we've gotten out of standard data and tensor parallel methods
The age of LLM's while revolutionary, is over. I hope to see next gen models open sourced, imagine having a o1 to home where you can choose the thinking time. Profound.
a good part of o1 is still LLM text generation, it just gets an additional dimension where it can reflect on it's own output, analyze and proceed from there
No, it isn't doing next token prediction, it uses graph theory to traverse the possibilities and the outputs the best result from the traversal. An LLM was used as the reward system in an RL training run, though, but what we get is not from an LLM. OAI, or specifically Noam, explains it in the press release for o1 on their site, without going into technical details
It hasn't so much ended but rather evolved into other forms of modality besides plain text. LLMs are still gonna be around, but embedded in other complementary systems. And given o1's success, I definitely think there is still more room to grow.
Inference engines (LLM's) are just the first in stepping stones to better intelligence. Think about your thought process, or anyone's... we infer, then we learn some ground truth and reason on our original assumptions(inference). This gives us overall ground truth.
What future online learning systems need is some sort of ground truth, that is the path to true general intelligence.
Specifically, llm's, or better to say, inference engines alongside reasoning engines will usher in the next era. But I wish Zuckerberg would hook up BIG llama to an RL algorithm and give us a reasoning engine like o1. We can only dream.
Newbie here. Would using these newer trained models take the same resources, given that the llm is the same size?
For example, would llama3.2 7b and llama4 7b, require about the same resources and work at about the same speed?
if you’re using the same code, yes. But across generations, there are algorithmic improvements that approximate very similar math, but faster, allowing retraining of an old model to be faster/use less conpute
It depends... on a lot of things.
First of all, the parameter count (7B) is sometimes rounded.
Second, some models use more vram for the context than others, though if you keep the context very small (like 1K) this isn't an issue.
Third, some models *quantize* more poorly than others. This is more of a "soft" factor that effectively makes the models a little bigger.
It's also possible the architecture will change dramatically (eg be mamba + transformers, bitnet, or something) which could dramatically change the math.
Yes if they are the same architecture and the same number of parameters and if we were just talking dense models they are going to take the same number of resources. There's more complexity to answer but in general this holds true.
He dropped it a while ago:
[https://www.perplexity.ai/page/llama-4-will-need-10x-compute-wopfuXfuQGq9zZzodDC0dQ](https://www.perplexity.ai/page/llama-4-will-need-10x-compute-wopfuXfuQGq9zZzodDC0dQ)
See the interview here: [https://www.youtube.com/watch?v=oX7OduG1YmI](https://www.youtube.com/watch?v=oX7OduG1YmI)
I have to assume llama 4 training has started already, which means they must have built something beyond their current [dual 25k H100 datacenters](https://engineering.fb.com/2024/03/12/data-center-engineering/building-metas-genai-infrastructure/).
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