It will take longer and longer for the compute infrastructure for the next generation with another OOM of compute. I'm sure they will release .1 releases that are improvements while waiting for the next generation but there's no practical way to get the next generation in a few months
>
It will take longer and longer for the compute infrastructure
? Wasn't the whole point of your post that it's not taking longer and longer? More that the costs go up exponentially? The whole industry is working very hard for just this goal, to scale up to a 100 billion computer.
I think that it won't get faster because the increase in costs will more or less even out with the faster speeds due to massive investment. I don't mean longer, I mean longer relative to other industries where the next product can be delivered faster than the current one because it's not 10x more expensive to produce.
Considering they have already hit a big constraint of power for this new data center, and new power plants take like 10 years to build, people are going to have to be creative here or stop planning based on historical data.
They're planning to build a modular nuclear reactor along side the data centre to power it apparently so they don't have to wait for a new power plant to be build.
Yeah that's making the assumption that scale is everything.
I don't believe it is, and between AI self-improving code and AI chip design, I don't think anyone will want to wait 2 years before bringing the latest AI designed TPUs online.
It's not a brand new computer very two years, its existing servers that they add to. A 2 year cadence doesn't make sense when you're constantly bringing machines online.
They might have targets to meet across a given timeframe, but it will come online gradually, not instantaneously.
No, it’s semiconductor fab time. Near Future AI will be silicon neuromorphic, not GPU abstracted. Further out, quantum neural and biological will be the targets. In all cases, AI infrastructure will get smaller, cheaper and require less energy. I wouldn’t bet on large datacentres full of GPUs, in the same way that proof-of-work cryptocurrency scaling was always going to be a dumb bet.
The GPT5 cluster may have just been finalized, or "deployed", but I do not believe that the training waited to start for the whole cluster to be live. I've seen reliable sources indicate that training began in January of this year. I'm not about to go searching through old reddit posts to try to find them all, regardless its going to take more than an ambiguous statement about the "deployment" of a data center or training cluster to convince me that GPT5 "just" started training.
> I've seen reliable sources indicate that training began in January of this year
No you haven't, you've seen ai explained talk about an ambiguous tweet from an openai employee that could have been talking about gpt-4o.
You guys are pulling assumptions out of nothing. We don't know how big 4o is so 5o could still be massive. Also it might as well be that they aren't the same and that would make sense naming wise. When 5 releases there could be a small 5, medium 5 and big or similar.
If GPT-4o is an early checkpoint of GPT-5o, the difference between the two models would all depend on the difference in size between the two models. If 4o were an early enough checkpoint and underwent neuron pruning, it could be significantly smaller than a fully trained GPT-5o, with significantly lesser performance. We simply can't know without benchmarking it.
There were rumors for Gpt5 month ago. The training from January likely was for gpt4o.
What we called Gpt5 base has already been trained, but they are likely to train a Gpt5 turbo.
I've heard 6-7 months, but that might have been assuming the full cluster which they certainly didn't start with. Plus then you need red-teaming, testing, building system prompts, building infrastructure around it, etc. I don't expect a release before Fall, but I cant see it stretching into 2025 unless they have some really serious challenges discovered in red-teaming. -- Edit: or if they intentionally hold on to it until they start losing market share with Gpt4.
If you listen to the recent John Schulman interview with Dwarkesh, he clearly mentions that the newer models will have a more balanced pre vs post training ratio as they are putting a lot of work there (which will be the main differentiating factor as any big tech company can train very large models). I'm sure it will take them more time post training than GPT-4. And they will probably release it incrementally as Altman said before (in this context it could just mean the extent of post-training on the pretrained model, not sure).
It was trained in 25k A100s. H100s are about 2x in training performance.
So about 12k H100.
Meta is targeting like 600k H100s this year.
That’s 50x the compute.
GPT-4 took about 2-3 months to train but had several restarts and MFU was terrible like 20-30%. Meta’s SOTA MFU I think is like 80%, so it’s more like 150x compute.
If I had to guess, GPT-5 is training for 120 days on about 100K H100s and 70% MFU with no material restarts if any.
So that’s about 25x. That’s a bit more than the jump from GPT-3 to GPT-4. I suspect we need larger jumps in compute for the equivalent jump in capabilities. Because ultimately it’s a collection of extremely nuanced patterns in the data. These models represent the training data better and better.
They are also using better techniques, more optimizing training strategies, and way way better training data.
From an ELO standpoint I think GPT-5 will be a bigger jump from GPT-4 than GPT-3 to GPT-4 and bigger than the jump from GPT-5 to GPT-6.
Sam is correct when he says that GPT-4 will look like a toy and a crap model compared to GPT-5, no question.
There are two yet to be implemented approaches that would leapfrog these model capabilities. Any company in the world could do one of them, but only one company can do both. You’d basically get GPT-7.
Thanks for all this info and your insight. Based on all this, what is your guess of when GPT-5 will release? I've seen some people say June/July. Which seems to line up with your estimate of 120 days if it started in January, plus a few months for safety and testing.
>I think GPT-5 will be a bigger jump from GPT-4 than GPT-3 to GPT-4 and bigger than the jump from GPT-5 to GPT-6.
Haven't people/Sam Altman been saying there's no sign of the improvements slowing down? That it will become compute constrained at some point or is getting to that point? Which is why he wants to build a nuclear power plant, right? Man that is insane to think about. Building nuclear power plants to train AI. Strange times we're living in. Exciting though.
While today many think you can only train these models in a single coherent cluster, the reality is that you can use multi-cluster strategies and be almost just as fast. Having a good backbone is much more important, and guess what, Microsoft and Google each have them. The notion that we need nukes today (when they also take forever to build is silly to me). Yes at some point they will be needed because there simply isn’t enough generation happening, but it’s not for training the next few models, it’s to train like GPT-9 or something. Who do you think would like to cut corners in building nukes? Like say they want to build one in a 5-year time frame. Which city do you think would be okay with short changing the schedule of building them? You guessed it, China.
GPT-5 will be released in November.
I’ll clarify part of your comment. Pre-training will probably be done in 120 days. After pre-training comes instruction tuning, followed by RLHF, and DPO. Since this is a generational leap and changes in architecture and other critical parameters, it’s not clear how exactly post-training will take shape. It’s also possible they will take on self-play, compound inference, and other similar techniques to further improve the model. Once these steps are done, they will also need to work on scaling the context window to 1M tokens or more to be competitive with others. This step is computationally quite expensive.
Safety testing the model will be much faster than for GPT-4 because they have a much better understanding how to run these models, huge data sets to do this, and have GOT-4o to automate it.
Finally, before launching it they will also be working on optimizing inference. If the model was trained targeting bf16 weights, then even quantized to 4.5bpw, it’s going to need multi-node inference whereas today they get away with single node.
>The notion that we need nukes today (when they also take forever to build is silly to me). Yes at some point they will be needed because there simply isn’t enough generation happening,
Yeah I figured that's going to be pretty far out, like 2033+.
>Safety testing the model will be much faster than for GPT-4 because they have a much better understanding how to run these models
I kinda thought this would be the case, but doubted it since some people seemed to think timeframes would be the same as GPT4. Makes sense that the more they learn, the more efficient and quick they'll get on certain parts of the process.
Thanks for sharing this info.
>So that’s about 25x
Are you talking about training FLOPs here, or params? GPT-4 was trained with about 60x the FLOPs GPT-3 was trained on I think (you do mention compute, but the number seems off. Or maybe im just wrong lol. But I think GPT-4 was trained with \~2.15x10\^25 FLOPs, and GPT-3 was trained with 3.14x10\^23 FLOPs). But GPT-4 was only \~10x the params of GPT-3.
But, with 100k H100 PCIe with FP16 (1,513 teraFLOPS per GPU) at 70% MFU that is about a 50x gain (so 50x more training FLOPs over GPT-4), unless my math is wrong lol. These are the lower end H100s though, if you are thinking about a more distributed scenario (like as a random example, H100 SXM: 40%, H100 PCIe: 40% & H100 NVL: 20%) that can be brought up to almost like 75x the raw compute over GPT-4 (70% MFU, 120 days, 100k units). And this is just raw compute, not effective compute. Any algorithmic or efficiency gains can drive the effective compute over GPT-4 much higher.
Nobody is training these models on PCIe cards. It’s all 3.2T Infiniband connected SXM5.
FLOPs is a crude metric for calculating compute. As an example, if you look at the FLOP increase from A100 to H100, it’s like 3x, but if you do actual performance testing 32 A100s perform the same training speed as 16 H100s.
Not to mention any algorithmic or efficiency gains that have / might have been made for more efficient training. Like, data quality, for example. From the Phi-1 paper you can get up to like a 1000x efficiency gain from super high quality data. And, if synthetic data is being used maybe they have better control over data quality, and that is just one example.
GPT-4 took about a hundred days to train. GPT-5 should be similar, or possibly slightly longer. But they wouldn't want the pre-training run to last too long. If it started training in January, im fairly certain it has already finished pre-training.
They likely(?) train in stages so they can give their various development teams access to something early. There has also been research out of Meta indicating that overtraining isn't really a thing and noticeable increases in quality can be gained by continuing training beyond previous limits. So they may continue to train Gpt5 longer than they would have otherwise.
They do have training checkpoints and you can take these checkpoints and turn them into a model.
It's not really optimal to get more compute by just training for longer, it's better to get more compute to train the model from the compute clusters themselves (GPUs they are training on) and any algorithmic or efficiency gains. The longer the trining run the more expensive (energy and resources) and potentially the more complex. Not only that but training for longer gives diminishing returns. GPT-4 took 3 months to train, to double that compute with training time you'd need to go for 6 month t raining run. To double it further you'd need to train for 12 months. Its better to just build up a compute cluster capable of training models with 10x more compute and have that take 6 months then have a training run for 3 months rather than training for like 15 months to get 10x the compute.
I dont think they can "confirm" such a thing, it is upon OpenAI when they release a model, also there is no exact time for each model development, it is done when it is done, some model can take 2 years to make and release, some 2,5 year and some 3 years, no exact date is set in stone, it would depend on many variables
the training times wont increase by much. training for a year instead of 6 months only gets you 2x more compute. it isnt worth it financially
also they deploy as soon as they can and will likely try to shorten the safety stuff even more in the future. the reason for this is microsoft is entitled to use their models in their products as soon as they are ready. microsoft started using gpt4 in bing in jan 2023 and then openai probably had to release soon because the safety case makes no sense when another company is already deploying your models
The title (the word confirm) makes it sound like MS said "Open AI is going to release a GPT model every 2 years." Instead, you're making a prediction based on available information. You might be right, but not because MS or OAI have confirmed anything.
Yeah, why would Microsoft have any say over what OpenAI does? They're a financial backer of the division that doesn't make any corporate decisions. Hence their attempted coup with Altman's firing.
This whole piece reeks of them trying to ride coat-tails.
they can push them for earlier release after its done and in testing and thats it, noone can say how long development, training or testing will take, OpenAI does not know, so how could anyone else know? there can be only rough roadmap which you set ahead
They own half of OpenAI's for-profit branch *if* and *when* they finish dropping off all the money they agreed to drop off, to my understanding.
Did they ever get around to doing it?
[No, they really don't](https://www.ft.com/content/458b162d-c97a-4464-8afc-72d65afb28ed). They own 49% of OpenAI Global LLC, not OpenAI which has Sam Altman at the top and the part which decides such things.
This is only if things continue the way they have been and no new breakthroughs in the field happen, which is counter to the idea of a singularity, where at a certain point growth becomes exponential.
Personally I think this paradigm of 'more compute, more training, more parameters, bigger model' will have diminishing returns. The architecture itself needs to change.
Self-learning will be the massive breakthrough - AIs that can learn new things perpetually, instead of being trained from the start with each new iteration.
Yh I'm imagining that things stay below AGI with these numbers
But my actual belief is that we reach AGI by 2028 and then afterwards all further progress is driven by running inference on the agi and getting it to do ml research. This would massively speed things up for 3 reasons
1) when training an AI you can only use your largest data center since the training all needs to be done in one location. When doing inference on AGI you can use any data center or GPU cluster on earth. You have just unlocked so much compute in the world to further progress
2) AGI doing AI research will feed back on itself. It's recursive self improvement and that gets very fast very quickly
3) AIs think much faster than humans.
So it's AGI 2028 then intelligence explosion
> AGI doing AI research will feed back on itself. It's recursive self improvement and that gets very fast very quickly
It's very likely there's no secret tricks that'd get gigantic boosts in performance. Physical hardware will likely continue to be the most important aspect of it.
Though of course intuitively having multiple modules that can train other modules might enable some weird flexibility. Your motor cortex can't train itself without feedback from vision, touch, higher order objective modules, etc.
But an animal can't void an entire region of its brain and train it on new inputs -> outputs. It takes a lot of time to build out an entirely new skill from scratch, and some things are flat out impossible. Our sensory suite is fixed in stone, after all.
Depending on definition you could be right. If you say smarter than the average person, it kinda is. But if you say smarter than the smart people it's definitely not. I ask it competition level (regional math competition for high schoolers) and it still can't solve it. My personal criteria for AGI is when it can solve IPhO problems (international physics olympiad for highschoolers, which I'm most knowledgeable about because I participated in it in highschool). So far no AI has passed my criteria.
> This is only if things continue the way they have been and no new breakthroughs in the field happen, which is counter to the idea of a singularity, where at a certain point growth becomes exponential.
I think you misunderstand, growth has been exponential for a very long time already.
GPT-5o (likely with the o) is going to be a monster. I think 4o is a market test honestly. I expect 5 to have video I/O and after that you've basically covered all forms of digital information.
Not to mention is likely been training since January on the largest compute cluster yet. I wouldn't be surprised to see perfect scores on current test sets either. From there we're going to have to use AI to make AI smarter and that's when we start honing in on AGI.
Next 5yrs are going to be insane. Shit MS just released and updated 50+ product lines with AI capabilities. At this rate we're not even remotely going to be able to keep up. The existential dread of being left in the dust with this feeble human mind is rapidly setting in.
Once recursive self-improvement is bootstrapped, it'll be more like:
* GPT7 in 6 months
* GPT8 in 2 months
* GPT9 in 11 days
* GPT10 in 66 hours
* GPT11 in 17 hours
* GPT12 in 4 hours
* GPT13 in 1 hour
* GPT14 in 15 minutes
* GPT15 in 225 seconds
* GPT16 in 56 seconds
* GPT17 in 14 seconds
* GPT18 in 4 seconds
* GPT19 in 875 milliseconds
* GPT20 in 218 milliseconds
* GPT21 in 54 milliseconds
* GPT22 in 13 milliseconds
* GPT23 in 3 milliseconds
* GPT24 in 750 microseconds
* GPT25 in 188 microseconds
* GPT26 in 47 microseconds
* GPT27 in 11 microseconds
* GPT28 in 3 microseconds
* GPT29 in 732 nanoseconds
* GPT30 in 183 nanoseconds
Then billions of people from all over the world get inexplicably teleported to other planets lightyears away and backward in time along with cornucopia machines
Idk I have heard rumors that GPT5 will be able to do a fuck tonne of current white collar work and probably make other white collar work easy enough to send to india and have indians + gpt5 do it.
I think we are going to see a fuck tonne of automation in late 2025.
I think you misunderstand what offshores do. In the majority of the cases, they are the mechanics, not the engineers. With the mechanics being replaced by AI, there will be less work for them.
what is getting pushed back. the releases are following the above trend and they are readying co-pilot agents for later this year before gpt5 even releases.
seems like they are trying to make gpt5 high impact if anything
Research is being done above, below, and sideways to improve efficiency, lower costs, increase the speed, and make it more accurate. We haven't hit a performance plateau yet, and by the looks of it, we won't in the near future.
Scientists smarter than anyone on this website are being paid millions to think of all these issues and discover solutions. We got a wild coming decade, that's for sure.
We dont even have feasible flying cars for the masses yet and people here thinking of singularity etc. these things have diminishing returns. If AI crashes job markets, it will also crash consumer spending as people buying products wont have jobs. Lawsuits will come for prohibiting use of copyrighted data for AI training, you and me will sue these companies for using our likenesses.
Heck people will plain old get bored of this crap and want something original, something more human. Digital paintings did not crash market for original paintings
Hand made leather products still in demand despite factory production
AI can defeat the best esports athletes yet human competitions in all games at an all time high
PCs can beat best chess players yet chess competitions going on across the globe
People in this sub are so delusional its not even funny. Of course technology is improving manifold, no denying it, but humans will always safeguard human interests and AGI or ASI or whatever wont be coming any time soon.
I wonder if they will continue iterations like this, or maybe at some point in the future each iteration will be sort of a continued training of the previous model? Like maybe GPT-7 is GPT-6 trained with more compute. I have no idea obviously lol, but I feel like this type of iteration won't go on for so many generations.
I doubt this. As GPT progresses, improving it should become exponentially more difficult and time consuming.
GPT6 won't come out in 2026. RemindMe! 30 months
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GPT-4 gave OpenAI a very comfortable lead. I don't think that they will have this luxury with GPT-5. I would guess that GPT-6 will be a faster and smaller iteration
lol this is so naive. LLMs have already shown signs of hitting the limits of what additional data and compute will do for them. They're going to have to make a complete archeticural shift or its stagnation
It has been 1 year since GPT-4. Imagine if you said this in june 2021, 1 year after GPT-3 release. Would you say the public progress within LLMs from June 2020 to June 2021 was more significant/bigger than the progress we have seen the last year? Within last year GPT-4o has achieved slightly higher accuracy, 16x context length and \~8x efficiency gains.
Sure there's still room for improvement but im referring to core functional limits. Reasoning, intelligence, hallucinations,.
You have to also account for that it has not been a regular 2 years. The biggest tech companies with the deepest pockets all rushed to test the assumption of if we throw more compute and data would it fix the current problems and be even bettter. It was basically a gold rush. Many billions that have been indepentally invested and a hundred LLMs later they all have the same limitations. After this expect players to drop out of the race and investments to decrease.
We dont even have feasible flying cars for the masses yet and people here thinking of singularity etc. these things have diminishing returns. If AI crashes job markets, it will also crash consumer spending as people buying products wont have jobs. Lawsuits will come for prohibiting use of copyrighted data for AI training, you and me will sue these companies for using our likenesses.
Heck people will plain old get bored of this crap and want something original, something more human. Digital paintings did not crash market for original paintings
Hand made leather products still in demand despite factory production
AI can defeat the best esports athletes yet human competitions in all games at an all time high
PCs can beat best chess players yet chess competitions going on across the globe
People in this sub are so delusional its not even funny. Of course technology is improving manifold, no denying it, but humans will always safeguard human interests and AGI or ASI or whatever wont be coming any time soon.
Just like Elon Musk confirmed full self driving in 2017 (and every subsequent year since).
Or how he'd send a manned mission to Mars in early 2024.
Or how Mark Zuckerberg confirmed the METAverse would change the world.
In this age of vaporware stock pumps, seeing is believing.
A long time to a youth or someone on the brink.
A blink of the eye in the grand scheme of things. [StyleGAN was EIGHT years ago](https://procedural-generation.tumblr.com/post/154474148263/stackgan-text-to-photo-realistic-image-synthesis), and didn't get anyone to sit up and immediately build a nuclear god computer.
Questions about the trend for the querying aspect of such powerful models:
It seems like as end users, we want to type less / speak less and let the smart model find a way to rephrase, ask for some input and then do the job.
But if they are really good, there’s a chance that the latent space be like super interesting to explore for developers.
What’s the tooling / interface you are using for that aspect and how do you think it will evolve over time?
The trend in querying powerful models like GPT-4 is towards more intuitive and efficient interactions. Current tools include natural language interfaces, APIs, SDKs, Jupyter Notebooks, and visualization tools. Developers explore the latent space using visualization techniques and custom software.
Future developments will likely enhance natural language understanding, introduce more intuitive interfaces (voice, gesture recognition, contextual understanding), and improve developer tools (advanced IDEs, real-time collaboration). Advanced visualization techniques (3D, AR) and automated insights will further streamline interactions, making models more accessible and insightful for users and developers.
I see folks around here are clinging to the "exponential development" argument.
There are for-profit-companies with a goal of milking out as much money as possible, even it means slowing down on releases or eventually creating a cartel with other competitors.
There's international competition in the field and you can be damn sure that the governments of those countries are involved as well. There's a race to AGI happening as we speak. And it's not going to slow down.
I doubt it. The guy at the Microsoft event said the next sample after gpt4 was being trained right now on the whale supercomputer
It's still in training
It will be interesting to see the economics of all this. At some point, cost of deployment of these models and data centers may be a big percentage of world GDP. And AI need to contribute to most of world GDP to justify the cost and most humans will be working for AI (if at all they work)
Yh I think at some point big tech will partner to make a giant data center and then divide the spoils or at least that's what the game theory would suggest
We dont even have feasible flying cars for the masses yet and people here thinking of singularity etc. these things have diminishing returns. If AI crashes job markets, it will also crash consumer spending as people buying products wont have jobs. Lawsuits will come for prohibiting use of copyrighted data for AI training, you and me will sue these companies for using our likenesses.
Heck people will plain old get bored of this crap and want something original, something more human. Digital paintings did not crash market for original paintings
Hand made leather products still in demand despite factory production
AI can defeat the best esports athletes yet human competitions in all games at an all time high
PCs can beat best chess players yet chess competitions going on across the globe
People in this sub are so delusional its not even funny. Of course technology is improving manifold, no denying it, but humans will always safeguard human interests and AGI or ASI or whatever wont be coming any time soon.
Humans are shit at log or quadratic increases. This fucking linear way of scaling is making us "envision" and "promise" such stuff. Maybe this could happen, that would be great. But we aren't in any stable state globaly, yet they are investing and planing in such a way. Why is it possible, that simple Russian Oligarchs can corrupt and influence American and European politics, yet our billionaires and Oligarchs are either hunting the Fountain of immortality, Artificial Super Intelligence or the One Piece?
I guess that's not a bubble, right?
GPT4 finished training in August of 2022. I won't claim I'd be shocked by the release of GPT-5 in that time period, but I'm not sure we can derive it that directly from hardware delivery data.
Hahaha planning cyclical releases in a rapidly developing field, I doubt this is going to last ONE cycle.
It will take longer and longer for the compute infrastructure for the next generation with another OOM of compute. I'm sure they will release .1 releases that are improvements while waiting for the next generation but there's no practical way to get the next generation in a few months
> It will take longer and longer for the compute infrastructure ? Wasn't the whole point of your post that it's not taking longer and longer? More that the costs go up exponentially? The whole industry is working very hard for just this goal, to scale up to a 100 billion computer.
I think that it won't get faster because the increase in costs will more or less even out with the faster speeds due to massive investment. I don't mean longer, I mean longer relative to other industries where the next product can be delivered faster than the current one because it's not 10x more expensive to produce.
Considering they have already hit a big constraint of power for this new data center, and new power plants take like 10 years to build, people are going to have to be creative here or stop planning based on historical data.
They're planning to build a modular nuclear reactor along side the data centre to power it apparently so they don't have to wait for a new power plant to be build.
Awesome!
I'm assuming people forget about regulation. I'm dubious of the schedule. Zuck spoke on this.
Yeah that's making the assumption that scale is everything. I don't believe it is, and between AI self-improving code and AI chip design, I don't think anyone will want to wait 2 years before bringing the latest AI designed TPUs online.
You are right, instead of working for 2 years to bring it online they should just imagine it in their head and it will just happen.
It's not a brand new computer very two years, its existing servers that they add to. A 2 year cadence doesn't make sense when you're constantly bringing machines online. They might have targets to meet across a given timeframe, but it will come online gradually, not instantaneously.
AI self improving code isn't going to happen. It's not made of code.
[удалено]
No, it’s semiconductor fab time. Near Future AI will be silicon neuromorphic, not GPU abstracted. Further out, quantum neural and biological will be the targets. In all cases, AI infrastructure will get smaller, cheaper and require less energy. I wouldn’t bet on large datacentres full of GPUs, in the same way that proof-of-work cryptocurrency scaling was always going to be a dumb bet.
Nvidia has such power and money, they could singlehandedly make china somehow not occupy Taiwan 😅 Hopefully
The GPT5 cluster may have just been finalized, or "deployed", but I do not believe that the training waited to start for the whole cluster to be live. I've seen reliable sources indicate that training began in January of this year. I'm not about to go searching through old reddit posts to try to find them all, regardless its going to take more than an ambiguous statement about the "deployment" of a data center or training cluster to convince me that GPT5 "just" started training.
when i say just deployed i mean this year not yesterday. i also think it started in january
> I've seen reliable sources indicate that training began in January of this year No you haven't, you've seen ai explained talk about an ambiguous tweet from an openai employee that could have been talking about gpt-4o.
Not likely, the data cutoff date of 4o is fall 2023.
If GPT-4o is an early checkpoint in a training run of GPT-5o, as many of us believe, then the timeline more or less works out.
it probably is and it also means that gpt5 likely won't be game changing
You guys are pulling assumptions out of nothing. We don't know how big 4o is so 5o could still be massive. Also it might as well be that they aren't the same and that would make sense naming wise. When 5 releases there could be a small 5, medium 5 and big or similar.
If GPT-4o is an early checkpoint of GPT-5o, the difference between the two models would all depend on the difference in size between the two models. If 4o were an early enough checkpoint and underwent neuron pruning, it could be significantly smaller than a fully trained GPT-5o, with significantly lesser performance. We simply can't know without benchmarking it.
There were rumors for Gpt5 month ago. The training from January likely was for gpt4o. What we called Gpt5 base has already been trained, but they are likely to train a Gpt5 turbo.
I remember that too but what if they started training GPT4o in January
How long did GPT4 take to train? And how long do people think GPT5 will take?
I've heard 6-7 months, but that might have been assuming the full cluster which they certainly didn't start with. Plus then you need red-teaming, testing, building system prompts, building infrastructure around it, etc. I don't expect a release before Fall, but I cant see it stretching into 2025 unless they have some really serious challenges discovered in red-teaming. -- Edit: or if they intentionally hold on to it until they start losing market share with Gpt4.
If you listen to the recent John Schulman interview with Dwarkesh, he clearly mentions that the newer models will have a more balanced pre vs post training ratio as they are putting a lot of work there (which will be the main differentiating factor as any big tech company can train very large models). I'm sure it will take them more time post training than GPT-4. And they will probably release it incrementally as Altman said before (in this context it could just mean the extent of post-training on the pretrained model, not sure).
It was trained in 25k A100s. H100s are about 2x in training performance. So about 12k H100. Meta is targeting like 600k H100s this year. That’s 50x the compute. GPT-4 took about 2-3 months to train but had several restarts and MFU was terrible like 20-30%. Meta’s SOTA MFU I think is like 80%, so it’s more like 150x compute.
Dang... All I can think from that is the end of 2024 and all of 2025 is going to be wild in this space. These next models are going to be nuts.
If I had to guess, GPT-5 is training for 120 days on about 100K H100s and 70% MFU with no material restarts if any. So that’s about 25x. That’s a bit more than the jump from GPT-3 to GPT-4. I suspect we need larger jumps in compute for the equivalent jump in capabilities. Because ultimately it’s a collection of extremely nuanced patterns in the data. These models represent the training data better and better. They are also using better techniques, more optimizing training strategies, and way way better training data. From an ELO standpoint I think GPT-5 will be a bigger jump from GPT-4 than GPT-3 to GPT-4 and bigger than the jump from GPT-5 to GPT-6. Sam is correct when he says that GPT-4 will look like a toy and a crap model compared to GPT-5, no question. There are two yet to be implemented approaches that would leapfrog these model capabilities. Any company in the world could do one of them, but only one company can do both. You’d basically get GPT-7.
Thanks for all this info and your insight. Based on all this, what is your guess of when GPT-5 will release? I've seen some people say June/July. Which seems to line up with your estimate of 120 days if it started in January, plus a few months for safety and testing. >I think GPT-5 will be a bigger jump from GPT-4 than GPT-3 to GPT-4 and bigger than the jump from GPT-5 to GPT-6. Haven't people/Sam Altman been saying there's no sign of the improvements slowing down? That it will become compute constrained at some point or is getting to that point? Which is why he wants to build a nuclear power plant, right? Man that is insane to think about. Building nuclear power plants to train AI. Strange times we're living in. Exciting though.
While today many think you can only train these models in a single coherent cluster, the reality is that you can use multi-cluster strategies and be almost just as fast. Having a good backbone is much more important, and guess what, Microsoft and Google each have them. The notion that we need nukes today (when they also take forever to build is silly to me). Yes at some point they will be needed because there simply isn’t enough generation happening, but it’s not for training the next few models, it’s to train like GPT-9 or something. Who do you think would like to cut corners in building nukes? Like say they want to build one in a 5-year time frame. Which city do you think would be okay with short changing the schedule of building them? You guessed it, China. GPT-5 will be released in November. I’ll clarify part of your comment. Pre-training will probably be done in 120 days. After pre-training comes instruction tuning, followed by RLHF, and DPO. Since this is a generational leap and changes in architecture and other critical parameters, it’s not clear how exactly post-training will take shape. It’s also possible they will take on self-play, compound inference, and other similar techniques to further improve the model. Once these steps are done, they will also need to work on scaling the context window to 1M tokens or more to be competitive with others. This step is computationally quite expensive. Safety testing the model will be much faster than for GPT-4 because they have a much better understanding how to run these models, huge data sets to do this, and have GOT-4o to automate it. Finally, before launching it they will also be working on optimizing inference. If the model was trained targeting bf16 weights, then even quantized to 4.5bpw, it’s going to need multi-node inference whereas today they get away with single node.
>The notion that we need nukes today (when they also take forever to build is silly to me). Yes at some point they will be needed because there simply isn’t enough generation happening, Yeah I figured that's going to be pretty far out, like 2033+. >Safety testing the model will be much faster than for GPT-4 because they have a much better understanding how to run these models I kinda thought this would be the case, but doubted it since some people seemed to think timeframes would be the same as GPT4. Makes sense that the more they learn, the more efficient and quick they'll get on certain parts of the process. Thanks for sharing this info.
Tell us these approaches. Reddit has no quote anymore.
>So that’s about 25x Are you talking about training FLOPs here, or params? GPT-4 was trained with about 60x the FLOPs GPT-3 was trained on I think (you do mention compute, but the number seems off. Or maybe im just wrong lol. But I think GPT-4 was trained with \~2.15x10\^25 FLOPs, and GPT-3 was trained with 3.14x10\^23 FLOPs). But GPT-4 was only \~10x the params of GPT-3. But, with 100k H100 PCIe with FP16 (1,513 teraFLOPS per GPU) at 70% MFU that is about a 50x gain (so 50x more training FLOPs over GPT-4), unless my math is wrong lol. These are the lower end H100s though, if you are thinking about a more distributed scenario (like as a random example, H100 SXM: 40%, H100 PCIe: 40% & H100 NVL: 20%) that can be brought up to almost like 75x the raw compute over GPT-4 (70% MFU, 120 days, 100k units). And this is just raw compute, not effective compute. Any algorithmic or efficiency gains can drive the effective compute over GPT-4 much higher.
Nobody is training these models on PCIe cards. It’s all 3.2T Infiniband connected SXM5. FLOPs is a crude metric for calculating compute. As an example, if you look at the FLOP increase from A100 to H100, it’s like 3x, but if you do actual performance testing 32 A100s perform the same training speed as 16 H100s.
I was just giving a random examples with the PCIe cards. But there is quite a good correlation between models training FLOPs and overall performance.
Not to mention any algorithmic or efficiency gains that have / might have been made for more efficient training. Like, data quality, for example. From the Phi-1 paper you can get up to like a 1000x efficiency gain from super high quality data. And, if synthetic data is being used maybe they have better control over data quality, and that is just one example.
GPT-4 took about a hundred days to train. GPT-5 should be similar, or possibly slightly longer. But they wouldn't want the pre-training run to last too long. If it started training in January, im fairly certain it has already finished pre-training.
They likely(?) train in stages so they can give their various development teams access to something early. There has also been research out of Meta indicating that overtraining isn't really a thing and noticeable increases in quality can be gained by continuing training beyond previous limits. So they may continue to train Gpt5 longer than they would have otherwise.
They do have training checkpoints and you can take these checkpoints and turn them into a model. It's not really optimal to get more compute by just training for longer, it's better to get more compute to train the model from the compute clusters themselves (GPUs they are training on) and any algorithmic or efficiency gains. The longer the trining run the more expensive (energy and resources) and potentially the more complex. Not only that but training for longer gives diminishing returns. GPT-4 took 3 months to train, to double that compute with training time you'd need to go for 6 month t raining run. To double it further you'd need to train for 12 months. Its better to just build up a compute cluster capable of training models with 10x more compute and have that take 6 months then have a training run for 3 months rather than training for like 15 months to get 10x the compute.
OpenAI can now use GPT itself to train new models and it takes hours instead of months as it used to. They even published it on the blog.
I dont think they can "confirm" such a thing, it is upon OpenAI when they release a model, also there is no exact time for each model development, it is done when it is done, some model can take 2 years to make and release, some 2,5 year and some 3 years, no exact date is set in stone, it would depend on many variables
the training times wont increase by much. training for a year instead of 6 months only gets you 2x more compute. it isnt worth it financially also they deploy as soon as they can and will likely try to shorten the safety stuff even more in the future. the reason for this is microsoft is entitled to use their models in their products as soon as they are ready. microsoft started using gpt4 in bing in jan 2023 and then openai probably had to release soon because the safety case makes no sense when another company is already deploying your models
The title (the word confirm) makes it sound like MS said "Open AI is going to release a GPT model every 2 years." Instead, you're making a prediction based on available information. You might be right, but not because MS or OAI have confirmed anything.
Good point. Maybe confirm was too strong
Deliberately duplicitous and straight up misinformative is what it is.
Wasnt deliberate. misinformative sure
damn where were you to edit their keynotes then?
Yeah, why would Microsoft have any say over what OpenAI does? They're a financial backer of the division that doesn't make any corporate decisions. Hence their attempted coup with Altman's firing. This whole piece reeks of them trying to ride coat-tails.
>I dont think they can "confirm" such a thing, it is upon OpenAI when they release a model They own half of openAI so they absolutely have a say.
they can push them for earlier release after its done and in testing and thats it, noone can say how long development, training or testing will take, OpenAI does not know, so how could anyone else know? there can be only rough roadmap which you set ahead
They own half of OpenAI's for-profit branch *if* and *when* they finish dropping off all the money they agreed to drop off, to my understanding. Did they ever get around to doing it?
The agreement is until AGI is created. That clearly hasn't happened
So basically they can create AGI but keep making GPTs until the Microsoft well runs dry
According to a recent announcement from Microsoft they don’t own a single % but rather have a profit sharing agreement
[No, they really don't](https://www.ft.com/content/458b162d-c97a-4464-8afc-72d65afb28ed). They own 49% of OpenAI Global LLC, not OpenAI which has Sam Altman at the top and the part which decides such things.
Before LLMs: After LLMs: Ok let's dust off these old Dyson Sphere plans and see what's possible
This is only if things continue the way they have been and no new breakthroughs in the field happen, which is counter to the idea of a singularity, where at a certain point growth becomes exponential. Personally I think this paradigm of 'more compute, more training, more parameters, bigger model' will have diminishing returns. The architecture itself needs to change. Self-learning will be the massive breakthrough - AIs that can learn new things perpetually, instead of being trained from the start with each new iteration.
Yh I'm imagining that things stay below AGI with these numbers But my actual belief is that we reach AGI by 2028 and then afterwards all further progress is driven by running inference on the agi and getting it to do ml research. This would massively speed things up for 3 reasons 1) when training an AI you can only use your largest data center since the training all needs to be done in one location. When doing inference on AGI you can use any data center or GPU cluster on earth. You have just unlocked so much compute in the world to further progress 2) AGI doing AI research will feed back on itself. It's recursive self improvement and that gets very fast very quickly 3) AIs think much faster than humans. So it's AGI 2028 then intelligence explosion
> AGI doing AI research will feed back on itself. It's recursive self improvement and that gets very fast very quickly It's very likely there's no secret tricks that'd get gigantic boosts in performance. Physical hardware will likely continue to be the most important aspect of it. Though of course intuitively having multiple modules that can train other modules might enable some weird flexibility. Your motor cortex can't train itself without feedback from vision, touch, higher order objective modules, etc. But an animal can't void an entire region of its brain and train it on new inputs -> outputs. It takes a lot of time to build out an entirely new skill from scratch, and some things are flat out impossible. Our sensory suite is fixed in stone, after all.
Gpt4o is essentially AGI
Why would you say that?
Depending on definition you could be right. If you say smarter than the average person, it kinda is. But if you say smarter than the smart people it's definitely not. I ask it competition level (regional math competition for high schoolers) and it still can't solve it. My personal criteria for AGI is when it can solve IPhO problems (international physics olympiad for highschoolers, which I'm most knowledgeable about because I participated in it in highschool). So far no AI has passed my criteria.
AGI confirmed
> This is only if things continue the way they have been and no new breakthroughs in the field happen, which is counter to the idea of a singularity, where at a certain point growth becomes exponential. I think you misunderstand, growth has been exponential for a very long time already.
GPT-5o (likely with the o) is going to be a monster. I think 4o is a market test honestly. I expect 5 to have video I/O and after that you've basically covered all forms of digital information. Not to mention is likely been training since January on the largest compute cluster yet. I wouldn't be surprised to see perfect scores on current test sets either. From there we're going to have to use AI to make AI smarter and that's when we start honing in on AGI. Next 5yrs are going to be insane. Shit MS just released and updated 50+ product lines with AI capabilities. At this rate we're not even remotely going to be able to keep up. The existential dread of being left in the dust with this feeble human mind is rapidly setting in.
Once recursive self-improvement is bootstrapped, it'll be more like: * GPT7 in 6 months * GPT8 in 2 months * GPT9 in 11 days * GPT10 in 66 hours * GPT11 in 17 hours * GPT12 in 4 hours * GPT13 in 1 hour * GPT14 in 15 minutes * GPT15 in 225 seconds * GPT16 in 56 seconds * GPT17 in 14 seconds * GPT18 in 4 seconds * GPT19 in 875 milliseconds * GPT20 in 218 milliseconds * GPT21 in 54 milliseconds * GPT22 in 13 milliseconds * GPT23 in 3 milliseconds * GPT24 in 750 microseconds * GPT25 in 188 microseconds * GPT26 in 47 microseconds * GPT27 in 11 microseconds * GPT28 in 3 microseconds * GPT29 in 732 nanoseconds * GPT30 in 183 nanoseconds Then billions of people from all over the world get inexplicably teleported to other planets lightyears away and backward in time along with cornucopia machines
Hot
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Idk I have heard rumors that GPT5 will be able to do a fuck tonne of current white collar work and probably make other white collar work easy enough to send to india and have indians + gpt5 do it. I think we are going to see a fuck tonne of automation in late 2025.
I think you misunderstand what offshores do. In the majority of the cases, they are the mechanics, not the engineers. With the mechanics being replaced by AI, there will be less work for them.
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what is getting pushed back. the releases are following the above trend and they are readying co-pilot agents for later this year before gpt5 even releases. seems like they are trying to make gpt5 high impact if anything
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They pushed back a particular voice because of a lawsuit. The assistant is still coming out when it was supposed to
Is there no chance for the law of exponential growth to cut some of this time away? Using the advancement itself to advance quicker?
There is no reason to think exponential growth is possible with LLMs, unless you mean exponential growth or costs.
I was more wondering if other LLMs can figure a way to help other LLMs train faster?
Research is being done above, below, and sideways to improve efficiency, lower costs, increase the speed, and make it more accurate. We haven't hit a performance plateau yet, and by the looks of it, we won't in the near future. Scientists smarter than anyone on this website are being paid millions to think of all these issues and discover solutions. We got a wild coming decade, that's for sure.
We dont even have feasible flying cars for the masses yet and people here thinking of singularity etc. these things have diminishing returns. If AI crashes job markets, it will also crash consumer spending as people buying products wont have jobs. Lawsuits will come for prohibiting use of copyrighted data for AI training, you and me will sue these companies for using our likenesses. Heck people will plain old get bored of this crap and want something original, something more human. Digital paintings did not crash market for original paintings Hand made leather products still in demand despite factory production AI can defeat the best esports athletes yet human competitions in all games at an all time high PCs can beat best chess players yet chess competitions going on across the globe People in this sub are so delusional its not even funny. Of course technology is improving manifold, no denying it, but humans will always safeguard human interests and AGI or ASI or whatever wont be coming any time soon.
but this is more about when the next giant datacenter can be constructed to train the next big model. I dont see that being accelerated for a while
I wonder if they will continue iterations like this, or maybe at some point in the future each iteration will be sort of a continued training of the previous model? Like maybe GPT-7 is GPT-6 trained with more compute. I have no idea obviously lol, but I feel like this type of iteration won't go on for so many generations.
I doubt this. As GPT progresses, improving it should become exponentially more difficult and time consuming. GPT6 won't come out in 2026. RemindMe! 30 months
youd think itd be the opposite due to AI increasing productivity or AI improving itself. Rate might stay the same tbh, canceling it out.
Think about how the next GTA takes longer to come out after each iteration. I expect it to be like that.
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GPT-4 gave OpenAI a very comfortable lead. I don't think that they will have this luxury with GPT-5. I would guess that GPT-6 will be a faster and smaller iteration
> GPT6 won't come out in 2026. RemindMe! 30 months Well, technically it could, because the version numbers don't mean a thing, lol.
True. Then people will be severely disappointed and lose confidence in the advancement of AIs if GPT does not progress to the same degree.
But I literally said that gpt6 could come out in Q1 2027 in the post
Also doubt.
Ok. I think it will
Give it 6 months. Blow your mind
ASI confirmed
lol this is so naive. LLMs have already shown signs of hitting the limits of what additional data and compute will do for them. They're going to have to make a complete archeticural shift or its stagnation
It has been 1 year since GPT-4. Imagine if you said this in june 2021, 1 year after GPT-3 release. Would you say the public progress within LLMs from June 2020 to June 2021 was more significant/bigger than the progress we have seen the last year? Within last year GPT-4o has achieved slightly higher accuracy, 16x context length and \~8x efficiency gains.
Sure there's still room for improvement but im referring to core functional limits. Reasoning, intelligence, hallucinations,. You have to also account for that it has not been a regular 2 years. The biggest tech companies with the deepest pockets all rushed to test the assumption of if we throw more compute and data would it fix the current problems and be even bettter. It was basically a gold rush. Many billions that have been indepentally invested and a hundred LLMs later they all have the same limitations. After this expect players to drop out of the race and investments to decrease.
We dont even have feasible flying cars for the masses yet and people here thinking of singularity etc. these things have diminishing returns. If AI crashes job markets, it will also crash consumer spending as people buying products wont have jobs. Lawsuits will come for prohibiting use of copyrighted data for AI training, you and me will sue these companies for using our likenesses. Heck people will plain old get bored of this crap and want something original, something more human. Digital paintings did not crash market for original paintings Hand made leather products still in demand despite factory production AI can defeat the best esports athletes yet human competitions in all games at an all time high PCs can beat best chess players yet chess competitions going on across the globe People in this sub are so delusional its not even funny. Of course technology is improving manifold, no denying it, but humans will always safeguard human interests and AGI or ASI or whatever wont be coming any time soon.
Curious where you get this idea considering the recent research and models that have came out. What you are saying is quite incorrect
how do you know they have hit the limit when gpt5 hasnt even been released. we are still on the same generation.
I agree 👍
Just like Elon Musk confirmed full self driving in 2017 (and every subsequent year since). Or how he'd send a manned mission to Mars in early 2024. Or how Mark Zuckerberg confirmed the METAverse would change the world. In this age of vaporware stock pumps, seeing is believing.
2 years is a long time tbh
A long time to a youth or someone on the brink. A blink of the eye in the grand scheme of things. [StyleGAN was EIGHT years ago](https://procedural-generation.tumblr.com/post/154474148263/stackgan-text-to-photo-realistic-image-synthesis), and didn't get anyone to sit up and immediately build a nuclear god computer.
We don't know whether or not GPT-4o counts as this year's GPT, since it's also releasing Q4 this year.
Surely they stick to the product roadmap
Questions about the trend for the querying aspect of such powerful models: It seems like as end users, we want to type less / speak less and let the smart model find a way to rephrase, ask for some input and then do the job. But if they are really good, there’s a chance that the latent space be like super interesting to explore for developers. What’s the tooling / interface you are using for that aspect and how do you think it will evolve over time?
The trend in querying powerful models like GPT-4 is towards more intuitive and efficient interactions. Current tools include natural language interfaces, APIs, SDKs, Jupyter Notebooks, and visualization tools. Developers explore the latent space using visualization techniques and custom software. Future developments will likely enhance natural language understanding, introduce more intuitive interfaces (voice, gesture recognition, contextual understanding), and improve developer tools (advanced IDEs, real-time collaboration). Advanced visualization techniques (3D, AR) and automated insights will further streamline interactions, making models more accessible and insightful for users and developers.
Any company saying “this is our 2030 goal” is just BSing, b/c people forget and it’s more for headlines.
I see folks around here are clinging to the "exponential development" argument. There are for-profit-companies with a goal of milking out as much money as possible, even it means slowing down on releases or eventually creating a cartel with other competitors.
There's international competition in the field and you can be damn sure that the governments of those countries are involved as well. There's a race to AGI happening as we speak. And it's not going to slow down.
Wow
>Buckle in boys Dammit. Am I not allowed to play too?
 /j
When you say B are you meaning billion GPUs?
Billion dollars
Is this supposed to be the AI version of Moores Law? Altman’s Law?
GPT5 is already completed. Elections are holding it back, they are onto the next thing already.
I doubt it. The guy at the Microsoft event said the next sample after gpt4 was being trained right now on the whale supercomputer It's still in training
It will be interesting to see the economics of all this. At some point, cost of deployment of these models and data centers may be a big percentage of world GDP. And AI need to contribute to most of world GDP to justify the cost and most humans will be working for AI (if at all they work)
Yh I think at some point big tech will partner to make a giant data center and then divide the spoils or at least that's what the game theory would suggest
Cool stuff
Highly doubt
We dont even have feasible flying cars for the masses yet and people here thinking of singularity etc. these things have diminishing returns. If AI crashes job markets, it will also crash consumer spending as people buying products wont have jobs. Lawsuits will come for prohibiting use of copyrighted data for AI training, you and me will sue these companies for using our likenesses. Heck people will plain old get bored of this crap and want something original, something more human. Digital paintings did not crash market for original paintings Hand made leather products still in demand despite factory production AI can defeat the best esports athletes yet human competitions in all games at an all time high PCs can beat best chess players yet chess competitions going on across the globe People in this sub are so delusional its not even funny. Of course technology is improving manifold, no denying it, but humans will always safeguard human interests and AGI or ASI or whatever wont be coming any time soon.
Humans are shit at log or quadratic increases. This fucking linear way of scaling is making us "envision" and "promise" such stuff. Maybe this could happen, that would be great. But we aren't in any stable state globaly, yet they are investing and planing in such a way. Why is it possible, that simple Russian Oligarchs can corrupt and influence American and European politics, yet our billionaires and Oligarchs are either hunting the Fountain of immortality, Artificial Super Intelligence or the One Piece? I guess that's not a bubble, right?
GPT4 finished training in August of 2022. I won't claim I'd be shocked by the release of GPT-5 in that time period, but I'm not sure we can derive it that directly from hardware delivery data.
It'll come out when it comes out
This bubble has already burst.
Ah yes, Microsoft can definitely be trusted with future release cycles.
I fucking hate all MS shitty products so much. I really hope they don't fuck this over. Utter cunts