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They could, would and should. But: Training a state of the art LLM costs millions in GPU, electricity alone. There is no "open" organization at this point that

They could, would and should. But: Training a state of the art LLM costs millions in GPU, electricity alone. There is no "open" organization at this point that can cover this. Current "open source public models" are shared by big players like Meta to undermine the competition. And they only publish their weights, not the training data, training protocols, training code; meaning it's not reproducible, and questionable if the training data is kosher.

sebmellen 2y ago
Doesn’t Deepseek somewhat counter this narrative?
- htrp 2y ago
  Don't they have something like 10k plus current gen GPUs?
lz400 2y ago
I understood SETI style meaning crowdsourced. Instead of mining bitcoin you mine LLMs. It's a nice idea I think. Not sure about technical details, bandwidth limitations, performance, etc.
- Mountain_Skies 2y ago
  SETI had a clear purpose that donors of computer resources could get behind. The LLM corps early on decided to drink the steering poison that will keep there from ever being a united community for making open LLMs. At best you'll get a fractured world of different projects, each with its own steering directives.
  - Aerroon 2y ago
    The internet is for ____. That could be a factor that unites enough people to donate their compute time to build diffusion models. At least if it was easy enough to set up.
    - CaptainFever 2y ago
      Related: people donating computing power to run diffusion and text models, which is definitely largely used for porn. https://stablehorde.net/ https://stablehorde.net/ Or the large amounts of community efforts (not exactly crowd sourced though) for diffusion fine-tunes and tools! Pony XL, and other uncensored models, for example. I haven't kept up with the rest, because there's just too much.
      - lostmsu 2y ago
        You don't have to donate, we will pay you for idle time of your gaming GPU: https://borg.games/setup https://borg.games/setup
- HPsquared 2y ago
  Unfortunately, LLM training is not as computationally easy (embarrassingly parallel) as mining bitcoins.
  - lz400 2y ago
    damn it! but nice research area
  - ghxst 2y ago
    If that were to be solved (if at all possible, and feasible / competitive) I can definitely see "LLM mining" be a historic milestone. Also much closer to the spirit of F@H in some sense, depending how you look at it. Would there be a financial incentive? And how would it be distributed? Could you receive a stake in the LLM proportional to the contribution you did? Would that be similar in some sense to purchasing stock in an AI company, or mining tokens for a crypto currency? Potentially a lot of opportunity here.
    - HPsquared 2y ago
      The models are too large to fit on a desktop GPU's VRAM. Progress would either require smaller models (MoE might help here? not sure) or bigger VRAM. For example training a 70 billion parameter model would require at least 140GB of VRAM in each system, whereas a large desktop GPU (4090) has only 24GB. You need enough memory to run the unquantized model for training, then stream the training data through - that part is what is done in parallel, farming out different bits of training data to each machine.
      - mr_toad 2y ago
        Data parallel training is not the only approach. Sometimes the model itself needs to be distributed across multiple GPU. https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/ https://www.microsoft.com/en-us/research/blog/zero-deepspeed... The communications overhead of doing this over the internet might be unworkable though.
myrmidon 2y ago
I think it's important to remember that we know neural networks can be trained to a very useful state from scratch for 24 GJ: This is 25 W for 30 years (or 7000 kWh, or a good half ton of diesel fuel), which is what a human brain consumes until adulthood. Even though our artificial training efficiency is worse now, likely to stay worse because we want to trade efficiency for faster training, and because we want to cram more knowledge into our training data than a human would be exposed to, it still seems likely to me that we'll get within orders of magnitude of this sooner or later. Even if our training efficiency topped out at a hundred times worse than a biological system, that would be the energy equivalent of <100 tons of diesel fuel. Compared to raising and educating a human (and also considering this training can the be utilized for billions of queries before it becomes obsolete) that strikes me as a very reasonable cost (especially compared to the amounts of energy we wasted on cryptocurrency mining without blinking an eye...)
- dbspin 2y ago
  This misses that evolution has been pre-training the human cognitive architecture - brain, limbic system, sympathetic and parasympathetic nervous systems, coevolved viral and bacterial ecosystems - for millions of years. We're not a tabula rasa training at birth to perfectly fit whatever set of training data we're presented. Far from it. Human learning is more akin to RAG, or test time training - specialising a heavily pre-trained model. It's not that we're born with very much knowledge, it's more that we're heavily specialised to acquire and retain certain kinds of knowledge and behaviour that are adaptive in the EEA (environment of evolutionary adaptedness). If the environment then doesn't provide the correct triggers at the correct times for activation of various learning mechanisms - best known being the critical period for language acquisition, we don't unfold into fully trained creatures. Bear in mind also that the social environment is vital both for human learning and functioning - we learn in the emotional, cognitive and resource provision context of other humans. And what we learn are behaviours that are effective in that context. Even in adulthood, the quickest way to make our cognitive architecture break down is to deny us social contact (hence the high rates of 'mental illness' in solitary confinement).
  - ben_w 2y ago
TZubiri 2y ago
Asking to share the training data is a bit too much, it's petabytes of data, probably has privacy implications. You can study and reproduce with your own training data right?
- agentultra 2y ago
  Probably legals ones too. Such aa evidence of copyright infringement.

sebmellen 2y ago

Doesn’t Deepseek somewhat counter this narrative?

htrp 2y ago
Don't they have something like 10k plus current gen GPUs?

lz400 2y ago

I understood SETI style meaning crowdsourced. Instead of mining bitcoin you mine LLMs. It's a nice idea I think. Not sure about technical details, bandwidth limitations, performance, etc.

Mountain_Skies 2y ago
SETI had a clear purpose that donors of computer resources could get behind. The LLM corps early on decided to drink the steering poison that will keep there from ever being a united community for making open LLMs. At best you'll get a fractured world of different projects, each with its own steering directives.
- Aerroon 2y ago
  The internet is for ____. That could be a factor that unites enough people to donate their compute time to build diffusion models. At least if it was easy enough to set up.
  - CaptainFever 2y ago
    Related: people donating computing power to run diffusion and text models, which is definitely largely used for porn. https://stablehorde.net/ https://stablehorde.net/ Or the large amounts of community efforts (not exactly crowd sourced though) for diffusion fine-tunes and tools! Pony XL, and other uncensored models, for example. I haven't kept up with the rest, because there's just too much.
    - lostmsu 2y ago
      You don't have to donate, we will pay you for idle time of your gaming GPU: https://borg.games/setup https://borg.games/setup
HPsquared 2y ago
Unfortunately, LLM training is not as computationally easy (embarrassingly parallel) as mining bitcoins.
- lz400 2y ago
  damn it! but nice research area
- ghxst 2y ago
  If that were to be solved (if at all possible, and feasible / competitive) I can definitely see "LLM mining" be a historic milestone. Also much closer to the spirit of F@H in some sense, depending how you look at it. Would there be a financial incentive? And how would it be distributed? Could you receive a stake in the LLM proportional to the contribution you did? Would that be similar in some sense to purchasing stock in an AI company, or mining tokens for a crypto currency? Potentially a lot of opportunity here.
  - HPsquared 2y ago
    The models are too large to fit on a desktop GPU's VRAM. Progress would either require smaller models (MoE might help here? not sure) or bigger VRAM. For example training a 70 billion parameter model would require at least 140GB of VRAM in each system, whereas a large desktop GPU (4090) has only 24GB. You need enough memory to run the unquantized model for training, then stream the training data through - that part is what is done in parallel, farming out different bits of training data to each machine.
    - mr_toad 2y ago
      Data parallel training is not the only approach. Sometimes the model itself needs to be distributed across multiple GPU. https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/ https://www.microsoft.com/en-us/research/blog/zero-deepspeed... The communications overhead of doing this over the internet might be unworkable though.

myrmidon 2y ago

I think it's important to remember that we know neural networks can be trained to a very useful state from scratch for 24 GJ: This is 25 W for 30 years (or 7000 kWh, or a good half ton of diesel fuel), which is what a human brain consumes until adulthood. Even though our artificial training efficiency is worse now, likely to stay worse because we want to trade efficiency for faster training, and because we want to cram more knowledge into our training data than a human would be exposed to, it still seems likely to me that we'll get within orders of magnitude of this sooner or later. Even if our training efficiency topped out at a hundred times worse than a biological system, that would be the energy equivalent of <100 tons of diesel fuel. Compared to raising and educating a human (and also considering this training can the be utilized for billions of queries before it becomes obsolete) that strikes me as a very reasonable cost (especially compared to the amounts of energy we wasted on cryptocurrency mining without blinking an eye...)

dbspin 2y ago
This misses that evolution has been pre-training the human cognitive architecture - brain, limbic system, sympathetic and parasympathetic nervous systems, coevolved viral and bacterial ecosystems - for millions of years. We're not a tabula rasa training at birth to perfectly fit whatever set of training data we're presented. Far from it. Human learning is more akin to RAG, or test time training - specialising a heavily pre-trained model. It's not that we're born with very much knowledge, it's more that we're heavily specialised to acquire and retain certain kinds of knowledge and behaviour that are adaptive in the EEA (environment of evolutionary adaptedness). If the environment then doesn't provide the correct triggers at the correct times for activation of various learning mechanisms - best known being the critical period for language acquisition, we don't unfold into fully trained creatures. Bear in mind also that the social environment is vital both for human learning and functioning - we learn in the emotional, cognitive and resource provision context of other humans. And what we learn are behaviours that are effective in that context. Even in adulthood, the quickest way to make our cognitive architecture break down is to deny us social contact (hence the high rates of 'mental illness' in solitary confinement).
- ben_w 2y ago

TZubiri 2y ago

Asking to share the training data is a bit too much, it's petabytes of data, probably has privacy implications. You can study and reproduce with your own training data right?

agentultra 2y ago
Probably legals ones too. Such aa evidence of copyright infringement.