What’s stopping people from using open sourced trained models? Like, why are tokens going to be needed in the future? Isn’t this their whole business model?
Honestly, I have a little tin-foil-hat theory that the used their infine supply of venture to fuck the memory market to make sure people couldn’t run giant chinese models on solar and eat their lunch.
training a top-level model takes harvesting petabytes / exabytes of revelant data and then months of continuous 100% power on a large server farm costing many millions of dollars.
It’s a one-and-done process, then the normal Q&A usage has vastly lower requirements.
Local models generally means you download the result of all that , after it’s done, (the PT in ChatGPT stands for pre-trained) and then generate answers locally, but the training wasn’t done locally. The generating answers step is called “inference”.
Hardware cost to do inference with a top-level model is within reach of most small businesses. Hardware cost to do training of a top-level model is orders of magnitude higher.
So, there are open-source models that are trained by universities, and then used by affluent but not billionaire people at home.
There are open-source models that can do inference (answer questions) on a typical 8GB gaming graphics card, but they aren’t in the same order of magnitude of quality as the famous top-level models.
Thank you! That’s the best answer I’ve seen on any platform!
What would you recommend as a local model? Want to dip my toes in but I’m woefully ignorant. And again, I’ve done my time in IT, so you don’t have to dumb it down, uh, too much.
They’re the output of all that data centre processing. The model is fixed at that point and can’t “learn” anything more, so the pressure is always on to release a newer model that resulted from more data with better algorithms, but still small enough t9 be able to run on a consumer grade GPU
The hardware requirements are substantial. If companies or individuals want to run models locally, especially at the speed you’d expect from the big name models, then everyone needs a pretty serious GPU on their work station. That or the company needs to build and maintain a server capable of running whatever loads they expect to see.
None of this is impossible of course. But you pay for the tokens one way or another. Either you pay the upfront cost of the hardware and continuing cost of the electricity to run it, or you pay with the inconvenience of running a model very slowly on lower end hardware, or you pay a subscription for someone else to worry about all that.
This is why local models are still niche. Only hobbyists nerds are willing to go through the trouble of setting up and running a local model. Most companies still are not sure if AI is actually worth it for them. So it’s much easier to pay for tokens than the giant upfront cost required to build you own infrastructure.
If the numbers the AI companies are putting up as far as infrastructure cost vs revenue are anything to go by, it probably isn’t worth it to most companies.
For enterprise, I think same reasons why companies using paid software instead of open software. Someone else to maintain and blamed if something doesn’t work.
They don’t need to, OpenAI and Anthropic are running at a huge loss to keep it cheaper to use them than to buy the comparable hardware required to replace them. The only way running open source models is cheaper is if you ignore hardware costs, or if you have a very specific and unusual usage pattern (i.e. constant 100% capacity, and not a clock cycle more or less), or if you go with another cloud provider who are also running at a loss.
If you want it well rounded I can see the argument for enterprise but for me personally I programmed one on my workstation and it’s trained on very specific things I think it barely uses up all 24gb vram I have available.
come on, I’ve trained toy size models too, you can’t seriously say they’re even vaguely approaching the capability of models that takes a whole massive server farm weeks or months to train.
Right, and what proportion of enterprise use cases genuinely need an appreciable fraction of that power (because they are wide ranging and non-repeating)?
Anyone that says these things aren’t impressive is kidding themselves. But how do we get from impressive to productivity enhancing in excess of cost? I find smaller specialised models more plausible on that front, but jury is still out on the financials, we’ll find out I guess!
What’s stopping people from using open sourced trained models? Like, why are tokens going to be needed in the future? Isn’t this their whole business model?
You need 200GB of video memory :)
Honestly, I have a little tin-foil-hat theory that the used their infine supply of venture to fuck the memory market to make sure people couldn’t run giant chinese models on solar and eat their lunch.
training a top-level model takes harvesting petabytes / exabytes of revelant data and then months of continuous 100% power on a large server farm costing many millions of dollars.
That was my understanding. So how do local models work? (Been fucking with computers since the 80s, AI is all new to me.)
It’s a one-and-done process, then the normal Q&A usage has vastly lower requirements.
Local models generally means you download the result of all that , after it’s done, (the PT in ChatGPT stands for pre-trained) and then generate answers locally, but the training wasn’t done locally. The generating answers step is called “inference”.
Hardware cost to do inference with a top-level model is within reach of most small businesses. Hardware cost to do training of a top-level model is orders of magnitude higher.
So, there are open-source models that are trained by universities, and then used by affluent but not billionaire people at home.
There are open-source models that can do inference (answer questions) on a typical 8GB gaming graphics card, but they aren’t in the same order of magnitude of quality as the famous top-level models.
Thank you! That’s the best answer I’ve seen on any platform!
What would you recommend as a local model? Want to dip my toes in but I’m woefully ignorant. And again, I’ve done my time in IT, so you don’t have to dumb it down, uh, too much.
They’re the output of all that data centre processing. The model is fixed at that point and can’t “learn” anything more, so the pressure is always on to release a newer model that resulted from more data with better algorithms, but still small enough t9 be able to run on a consumer grade GPU
The hardware requirements are substantial. If companies or individuals want to run models locally, especially at the speed you’d expect from the big name models, then everyone needs a pretty serious GPU on their work station. That or the company needs to build and maintain a server capable of running whatever loads they expect to see.
None of this is impossible of course. But you pay for the tokens one way or another. Either you pay the upfront cost of the hardware and continuing cost of the electricity to run it, or you pay with the inconvenience of running a model very slowly on lower end hardware, or you pay a subscription for someone else to worry about all that.
This is why local models are still niche. Only hobbyists nerds are willing to go through the trouble of setting up and running a local model. Most companies still are not sure if AI is actually worth it for them. So it’s much easier to pay for tokens than the giant upfront cost required to build you own infrastructure.
If the numbers the AI companies are putting up as far as infrastructure cost vs revenue are anything to go by, it probably isn’t worth it to most companies.
For enterprise, I think same reasons why companies using paid software instead of open software. Someone else to maintain and blamed if something doesn’t work.
They don’t need to, OpenAI and Anthropic are running at a huge loss to keep it cheaper to use them than to buy the comparable hardware required to replace them. The only way running open source models is cheaper is if you ignore hardware costs, or if you have a very specific and unusual usage pattern (i.e. constant 100% capacity, and not a clock cycle more or less), or if you go with another cloud provider who are also running at a loss.
They thought they sold tokens, they actually sell marginally (6 months lead) improved models.
If you want it well rounded I can see the argument for enterprise but for me personally I programmed one on my workstation and it’s trained on very specific things I think it barely uses up all 24gb vram I have available.
come on, I’ve trained toy size models too, you can’t seriously say they’re even vaguely approaching the capability of models that takes a whole massive server farm weeks or months to train.
Right, and what proportion of enterprise use cases genuinely need an appreciable fraction of that power (because they are wide ranging and non-repeating)?
Anyone that says these things aren’t impressive is kidding themselves. But how do we get from impressive to productivity enhancing in excess of cost? I find smaller specialised models more plausible on that front, but jury is still out on the financials, we’ll find out I guess!