> MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines. For each layer, we stream parameters in and compute gradients out, minimizing persistent device state
This is pretty awesome. The only compute I have at home is an RTX 3080 with 10 GB of VRAM, so I struggle with training larger models (>40M, 50M params). I get OOM errors and have to optimize a lot.
I have a lot more CPU RAM in my PC, and this would likely increase the size of models I can train locally.
To make the most of these architectures I think the key is essentially moving more of the knowledge/capabilities out of the "weights" and into the complimentary parts of the system in a way that's proportionate to the capabilities of the hardware.
In the past couple months there's been a kind of explosion in small-models that are occupying a niche in this kind of AI-transcoding space. What I'm hoping we're right on the cusp of achieving is a similar explosion in what I'd call tool-adaptation, where an LLM paired with some mostly-fixed suite of tools and problem cases can trade off some generality for a specialized (potentially hyper-specialized to the company or user) role.
The thing about more transcoding-related tasks is that they in general stay in sync with what the user of the device is actively doing, which will also typically be closely aligned with the capabilities of the user's hardware and what they want to do with their computer. So most people aren't being intentional about this kind of stuff right now, partly out of habit I think, because only just now does it make sense to think of personal computer as "stranded hardware" now that they can be steered/programmed somewhat autonomously.
I'm wondering if with the right approach to MoE on local devices (which local llms are heading towards) we could basically amortize the expensive hit from loading weights in and out of VRAM through some kind of extreme batch use case that users still find useful enough to be worth the latency. LoRa is already really useful for this but obviously sometimes you need more expertise/specialization than just a few layers' difference. Experimenting with this right now. It's the same basic principle as in the paper except less of a technical optimization and more workload optimization. Also it's literally the beginning of machine culture so that's kind of cool
> To make the most of these architectures I think the key is essentially moving more of the knowledge/capabilities out of the "weights" and into the complimentary parts of the system in a way that's proportionate to the capabilities of the hardware
I think that's only possible to limited extent. Learnt skills (RL in context of an LLM?) need to be in the weights of the model since this reflects the model's "personalized" learning of the behavioral feedback loop. Declarative knowledge (facts) can be loaded at runtime (RAG).
That's interesting. So you want to train language, linguistic reasoning, and tool use, but otherwise strip out all knowledge in lieu of a massive context? Just grade they model on how well it can access local information, perhaps also run tools?
The claims of the article assumes far more compute and far more VRAM..while the trick enables less back and forth, they don't eliminate it.
I doubt you meant 50M. Rather 50B?
You can only give it a try, but don't get your hopes high on a large context. If their technique works I would guess 8096k context limits would still OOM. 2048 maybe.
I'm extrapolating based on my experiment without this paper's trick to leverage the system memory.
> You can only give it a try, but don't get your hopes high on a large context.
You may or may not know this, but: when training off-the-shelf LLMs (i.e. ones which have a huge vocabulary) what consumes a huge amount of memory usage is calculating the cross-entropy loss (which gets worse the more tokens you stuff in your batch), so always use a fused cross-entropy kernel.
For example, for a Gemma 2 model with 2B parameters at a batch size of 8k this consumes 24GB of VRAM by default (!); you can fuse your cross-entropy loss with @torch.compile and that can cut down this memory usage to something like a few gigabytes, but with a dedicated kernel this becomes a few megabytes.
> "The integration involves modifying the TransformerDecoder module in torchtune to bypass the linear layer computation, allowing the Liger Fused Linear Cross Entropy Loss to handle the forward projection weights. "
Although this wasn't integrated into PyTorch itself (but to torchtune, which is a different thing). If you're writing your own training loop you need to use a third-party kernel, e.g. the Liger kernel mentioned in the article, or Cut Cross Entropy (which is much better than the Liger one, although IIRC it has a numeric bug in one of its kernels making the results very slightly off).
> Activation would still require gigabytes for a few kb context.
For that you use activation checkpointing, and you can also offload that to the CPU in a smart way to hide the latency. Although, yes, for long context training the activations do dominate the memory usage (and quantizing them degrades things more than just quantizing weights and/or optimizer states).
> This is pretty awesome. The only compute I have at home is an RTX 3080 with 10 GB of VRAM, so I struggle with training larger models (>40M, 50M params). I get OOM errors and have to optimize a lot.
I'm on the same GPU, its intimidating to me if I even want to bother training anything at all. Do you mind sharing what kind of training you've done with that GPU? :)
Make sure you are running adaptive cooling (or just bump them up) on the top fans in your case. Also, ensure that you are undervolting appropriately using something like MSI Afterburner or GreenWithEnvy.
If you don't, you could easily toast your RAM -- especially under BF16.
Anything that can run on a AMD395+ w/128GB or whatever the apple equivelent would break things wide open. Training a model on my frameworks of choice or our business info would be awesome.
This isn't really anything new; I've been doing something like this for quite a while, I just haven't bothered writing a paper. (: Probably anyone who would seriously tackle the problem of "how do I train a huge model on a tiny amount of VRAM?" would come up with something similar.
However, most people in the field don't, because the actual practical utility of training huge models on a single GPU is quite low. (e.g they got 341 tok/s for a 14B model on a single 3090 while with my method I was getting ~1k tok/s on a single 4090; that's still very slow)
Also, there are more tricks one can use to speed up training/lower VRAM usage which they're not using. For example, you don't need any gradient offloading (you can just accumulate the gradients directly into the optimizers' states if you modify your optimizer), you can use Muon instead of Adam (which needs only half of VRAM of Adam), you can use quantization (both for parameters and for the optimizer states; e.g. I found Muon quantized into 4-bit working relatively well), etc.
341 is two orders of magnitude faster than your 1 tok/s so it doesn’t seem like their stuff is all that obvious. I also have no baseline for training to know if 341tok/s is slow but it seems speedy for a 3090.
That’s a pretty non-standard H200 configuration. In the regular HGX configurations, a node with 8xH200 has that much CPU DRAM. That makes the title of the paper somewhat arguable imo.
even if it scales down, the host-to-gpu memory ratio is around 10 to 1 (1.5 TB vs 141 GB) [1].
might need some odd system builds, improbable with current pricing, to replicate or better the ratio. such as a ~256 GB system for a 4090 (with 24 GB VRAM).
Having just started to dabble with training LLMs, it seems training a model if you have a training and validation data set is fairly trivial. Creating a good and sufficiently large training and validation data set seems to be the hard part.
Sourcing, cleaning, curating, labeling, generating and quality controlling training data is hard and a lot of work, at least has been for the projects I've dabbled with.
I’m curious how this technique works, or not, with unified memory architectures such as Apple’s M series. It seems like it’s relying on using overlapping processes to help speed things up, but I would assume that having everything unified in main memory such that you don’t have to transfer everything back and forth to the GPU would also have some advantages. Can someone wiser explain this to me?
For FP16-native training of 100B+ models, you will probably still be offloading to swap unless you've got a $150,000 RDMA Mac Studio cluster. The workload would be deeply compute-constrained if you could fit it in-memory anyways.
This is a fantastic step toward democratizing large model training. Making 100B+ parameter training accessible on a single GPU could open the door to a lot more independent research. Really impressive work!
There isn't really such a thing as 'too slow' as an objective fact though. It depends on how much patience and money for electricity you have. In AI image gen circles I see people complaining if a model takes more than 5s to generate an image, and other people on very limited hardware who happily wait half an hour per image. It's hard to make a judgement call about what 'too slow' means. It's quite subjective.
If it would take so long to train that the model will be obsolete before the training is finished that might be considered too long. With ML you can definitely hit a point where it is too slow for any practical purpose.
Obsolete because of what? Because with limited hardware you’re never aiming for state of the art, and for fine-tuning, you don’t steer for too long anyway.
That’s just playing semantics. Nobody is talking about, “objective facts” or need define them here. If the step time is measured in days, and your model takes years to train, then it will never get trained to completion on consumer hardware (the entire point).
So distribute copies of the model in RAM to multiple machines, have each machine update different parts of the model weights, and sync updates over the network
FWIW Zero-3 refers to a common strategy for sharding model components across GPUs (commonly called FSDP-2, Full Sharded Data Parallel). The "3" is the level of sharding (how much stuff to distribute across GPUs, e.g. just weights, versus optimizer state as well, etc.)
I think OpenAI and Anthropic just downloaded the same torrents from Anna's Archive that anyone else can. But it's only OK when they do it. The rest of us get nastygrams from law offices. Anthropic actually had to cough up some bucks, for that matter.
At that point, a lot depends on the quality of the preprocessing applied to the raw text dumps. It is reportedly not that trivial to go from DumpOfSketchyRussianPirateSite.zip to a data set suitable for ingestion during pretraining. A few bad chunks of data can apparently do more harm than one would expect.
AFAIK Google scans almost everything in print as part of the Google Books initiative, so they may have been able to skip the torrenting step.
> I think OpenAI and Anthropic just downloaded the same torrents from Anna's Archive
I think you're underestimating how much chat conversation data they've gathered at this point, and how much of it is part of the training set.
None of that is available to anyone who wants to train a frontier model.
And when it comes to Google ... the hoard of data they're sitting on goes back to what? 1998? They've basically got a digital record of what happened since the birth of the internet.
The GPU is no longer the brain, it's the hand. The brain is your RAM. Suddenly that 256GB DDR5 build your wife questioned is 'research infrastructure.'
> MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines. For each layer, we stream parameters in and compute gradients out, minimizing persistent device state
This is pretty awesome. The only compute I have at home is an RTX 3080 with 10 GB of VRAM, so I struggle with training larger models (>40M, 50M params). I get OOM errors and have to optimize a lot.
I have a lot more CPU RAM in my PC, and this would likely increase the size of models I can train locally.
To make the most of these architectures I think the key is essentially moving more of the knowledge/capabilities out of the "weights" and into the complimentary parts of the system in a way that's proportionate to the capabilities of the hardware.
In the past couple months there's been a kind of explosion in small-models that are occupying a niche in this kind of AI-transcoding space. What I'm hoping we're right on the cusp of achieving is a similar explosion in what I'd call tool-adaptation, where an LLM paired with some mostly-fixed suite of tools and problem cases can trade off some generality for a specialized (potentially hyper-specialized to the company or user) role.
The thing about more transcoding-related tasks is that they in general stay in sync with what the user of the device is actively doing, which will also typically be closely aligned with the capabilities of the user's hardware and what they want to do with their computer. So most people aren't being intentional about this kind of stuff right now, partly out of habit I think, because only just now does it make sense to think of personal computer as "stranded hardware" now that they can be steered/programmed somewhat autonomously.
I'm wondering if with the right approach to MoE on local devices (which local llms are heading towards) we could basically amortize the expensive hit from loading weights in and out of VRAM through some kind of extreme batch use case that users still find useful enough to be worth the latency. LoRa is already really useful for this but obviously sometimes you need more expertise/specialization than just a few layers' difference. Experimenting with this right now. It's the same basic principle as in the paper except less of a technical optimization and more workload optimization. Also it's literally the beginning of machine culture so that's kind of cool
> To make the most of these architectures I think the key is essentially moving more of the knowledge/capabilities out of the "weights" and into the complimentary parts of the system in a way that's proportionate to the capabilities of the hardware
I think that's only possible to limited extent. Learnt skills (RL in context of an LLM?) need to be in the weights of the model since this reflects the model's "personalized" learning of the behavioral feedback loop. Declarative knowledge (facts) can be loaded at runtime (RAG).
That's interesting. So you want to train language, linguistic reasoning, and tool use, but otherwise strip out all knowledge in lieu of a massive context? Just grade they model on how well it can access local information, perhaps also run tools?
You are on the right track. Check out the Semiotic-Reflexive Transformer (SRT) here.
https://open.substack.com/pub/sublius/p/the-semiotic-reflexi...
Empirically proved a new science. Still gets downvoted xD.
The claims of the article assumes far more compute and far more VRAM..while the trick enables less back and forth, they don't eliminate it.
I doubt you meant 50M. Rather 50B?
You can only give it a try, but don't get your hopes high on a large context. If their technique works I would guess 8096k context limits would still OOM. 2048 maybe.
I'm extrapolating based on my experiment without this paper's trick to leverage the system memory.
> You can only give it a try, but don't get your hopes high on a large context.
You may or may not know this, but: when training off-the-shelf LLMs (i.e. ones which have a huge vocabulary) what consumes a huge amount of memory usage is calculating the cross-entropy loss (which gets worse the more tokens you stuff in your batch), so always use a fused cross-entropy kernel.
For example, for a Gemma 2 model with 2B parameters at a batch size of 8k this consumes 24GB of VRAM by default (!); you can fuse your cross-entropy loss with @torch.compile and that can cut down this memory usage to something like a few gigabytes, but with a dedicated kernel this becomes a few megabytes.
I'd not heard of this before, quick search turned up this 2025 post which suggests "fused cross-entropy loss" kernel was integrated into PyTorch:
https://pytorch.org/blog/peak-performance-minimized-memory/
Is this the same thing as you discuss above?Yes.
Although this wasn't integrated into PyTorch itself (but to torchtune, which is a different thing). If you're writing your own training loop you need to use a third-party kernel, e.g. the Liger kernel mentioned in the article, or Cut Cross Entropy (which is much better than the Liger one, although IIRC it has a numeric bug in one of its kernels making the results very slightly off).
Activation would still require gigabytes for a few kb context.
There are plenty of techniques to optimise. But the question is what can an rtx 3080 train before OOM. The answer is not that much.
Can barely do quantized fine tuning. Even then, small context.
> Activation would still require gigabytes for a few kb context.
For that you use activation checkpointing, and you can also offload that to the CPU in a smart way to hide the latency. Although, yes, for long context training the activations do dominate the memory usage (and quantizing them degrades things more than just quantizing weights and/or optimizer states).
Maybe they're not talking about LLMs? Training a 9M parameter YOLO can take over 20GB of VRAM if you use a large image size.
> This is pretty awesome. The only compute I have at home is an RTX 3080 with 10 GB of VRAM, so I struggle with training larger models (>40M, 50M params). I get OOM errors and have to optimize a lot.
I'm on the same GPU, its intimidating to me if I even want to bother training anything at all. Do you mind sharing what kind of training you've done with that GPU? :)
Make sure you are running adaptive cooling (or just bump them up) on the top fans in your case. Also, ensure that you are undervolting appropriately using something like MSI Afterburner or GreenWithEnvy.
If you don't, you could easily toast your RAM -- especially under BF16.
Anything that can run on a AMD395+ w/128GB or whatever the apple equivelent would break things wide open. Training a model on my frameworks of choice or our business info would be awesome.
Could I ask what you train your models to do? How do you generate the training data for it?
This isn't really anything new; I've been doing something like this for quite a while, I just haven't bothered writing a paper. (: Probably anyone who would seriously tackle the problem of "how do I train a huge model on a tiny amount of VRAM?" would come up with something similar.
However, most people in the field don't, because the actual practical utility of training huge models on a single GPU is quite low. (e.g they got 341 tok/s for a 14B model on a single 3090 while with my method I was getting ~1k tok/s on a single 4090; that's still very slow)
Also, there are more tricks one can use to speed up training/lower VRAM usage which they're not using. For example, you don't need any gradient offloading (you can just accumulate the gradients directly into the optimizers' states if you modify your optimizer), you can use Muon instead of Adam (which needs only half of VRAM of Adam), you can use quantization (both for parameters and for the optimizer states; e.g. I found Muon quantized into 4-bit working relatively well), etc.
As the saying goes, POC or GTFO
I invented faster than light travel, it was obvious, just didn't write a paper yet either :)
Can you take the time to write your methods? I’d be interested in reading it
341 is two orders of magnitude faster than your 1 tok/s so it doesn’t seem like their stuff is all that obvious. I also have no baseline for training to know if 341tok/s is slow but it seems speedy for a 3090.
OP said 1k, not 1
:) Coffee is good
1k tok/s = 1000 tok/s...
OOM is log10
> H200 GPU with 1.5TB host memory,
While yes it's one GPU.. It's not exactly a slim one.
That’s a pretty non-standard H200 configuration. In the regular HGX configurations, a node with 8xH200 has that much CPU DRAM. That makes the title of the paper somewhat arguable imo.
even if it scales down, the host-to-gpu memory ratio is around 10 to 1 (1.5 TB vs 141 GB) [1].
might need some odd system builds, improbable with current pricing, to replicate or better the ratio. such as a ~256 GB system for a 4090 (with 24 GB VRAM).
[1] https://www.nvidia.com/en-us/data-center/h200/
When the comparison is again 128 H100's , yeah, this is a crazy good upgrade.
And you can rent H100's and H200s for not that much per hour.
Yes but they are getting only 341 tok/s. A 2.5 trillion run would take over 200 years.
$2-4/hr always sounds cheap until you multiply by wall clock and reruns
>cries in RTX 3060
How long would it actually take to train a 120B model on an H200? What if you have 8?
Having just started to dabble with training LLMs, it seems training a model if you have a training and validation data set is fairly trivial. Creating a good and sufficiently large training and validation data set seems to be the hard part.
Sourcing, cleaning, curating, labeling, generating and quality controlling training data is hard and a lot of work, at least has been for the projects I've dabbled with.
I’m curious how this technique works, or not, with unified memory architectures such as Apple’s M series. It seems like it’s relying on using overlapping processes to help speed things up, but I would assume that having everything unified in main memory such that you don’t have to transfer everything back and forth to the GPU would also have some advantages. Can someone wiser explain this to me?
For FP16-native training of 100B+ models, you will probably still be offloading to swap unless you've got a $150,000 RDMA Mac Studio cluster. The workload would be deeply compute-constrained if you could fit it in-memory anyways.
I was wondering how well this would work :) You can definitely push this further, the question is: how well can the gradients and updates compress?
sounds very similar to https://docs.pytorch.org/docs/stable/distributed.fsdp.fully_... i wonder how much this could be replicated using only this pytorch primitive
Check out Fig. 6 in this paper, it shows the comparison between the proposed method and pytorch native FSDP offload method.
This is a fantastic step toward democratizing large model training. Making 100B+ parameter training accessible on a single GPU could open the door to a lot more independent research. Really impressive work!
This would likely only get used for small finetuning jobs. It’s too slow for the scale of pretraining.
It’s too slow for the scale of pretraining.
There isn't really such a thing as 'too slow' as an objective fact though. It depends on how much patience and money for electricity you have. In AI image gen circles I see people complaining if a model takes more than 5s to generate an image, and other people on very limited hardware who happily wait half an hour per image. It's hard to make a judgement call about what 'too slow' means. It's quite subjective.
If it would take so long to train that the model will be obsolete before the training is finished that might be considered too long. With ML you can definitely hit a point where it is too slow for any practical purpose.
Obsolete because of what? Because with limited hardware you’re never aiming for state of the art, and for fine-tuning, you don’t steer for too long anyway.
Because there is a new model that is better, faster, more refined, etc...
If your training time is measured in years or decades it probably won't be practical.
That’s just playing semantics. Nobody is talking about, “objective facts” or need define them here. If the step time is measured in days, and your model takes years to train, then it will never get trained to completion on consumer hardware (the entire point).
So distribute copies of the model in RAM to multiple machines, have each machine update different parts of the model weights, and sync updates over the network
decentralized training makes a lot more sense when the required hardware isn't a $40K GPU...
Seems similar to Microsoft DeepSpeed.
The compare against “DeepSpeed ZeRO-3” apparently.
FWIW Zero-3 refers to a common strategy for sharding model components across GPUs (commonly called FSDP-2, Full Sharded Data Parallel). The "3" is the level of sharding (how much stuff to distribute across GPUs, e.g. just weights, versus optimizer state as well, etc.)
I'm most likely wrong but large language models are literally just stealing....everything
Why is it no one ever talks about the one thing no one can get their hands on except the big labs ?
I'm talking about the training set.
Sure there are some open sets out there.
But my guess is they are nowhere near what OpenAI, Google and Anthropic are actually using.
Happy to be proven wrong.
I think OpenAI and Anthropic just downloaded the same torrents from Anna's Archive that anyone else can. But it's only OK when they do it. The rest of us get nastygrams from law offices. Anthropic actually had to cough up some bucks, for that matter.
At that point, a lot depends on the quality of the preprocessing applied to the raw text dumps. It is reportedly not that trivial to go from DumpOfSketchyRussianPirateSite.zip to a data set suitable for ingestion during pretraining. A few bad chunks of data can apparently do more harm than one would expect.
AFAIK Google scans almost everything in print as part of the Google Books initiative, so they may have been able to skip the torrenting step.
> I think OpenAI and Anthropic just downloaded the same torrents from Anna's Archive
I think you're underestimating how much chat conversation data they've gathered at this point, and how much of it is part of the training set.
None of that is available to anyone who wants to train a frontier model.
And when it comes to Google ... the hoard of data they're sitting on goes back to what? 1998? They've basically got a digital record of what happened since the birth of the internet.
The GPU is no longer the brain, it's the hand. The brain is your RAM. Suddenly that 256GB DDR5 build your wife questioned is 'research infrastructure.'