I mean I literally run a local LLM, while the model sits in memory it’s really not using up a crazy amount of resources, I should hook up something to actually measure exactly how much it’s pulling vs just looking at htop/atop and guesstimating based on load TBF.
Vs when I play a game and the fans start blaring and it heats up and you can clearly see the usage increasing across various metrics
There’s no functional difference aside from usage and scale, which is my point.
I find it interesting that the only actual energy calculations I see from researchers is the training and the things going along with the training, rather then the usage per actual request after training.
People then conflate training energy costs to normal usage cost without data to back it up. I don’t have the data either but I do have what I can do/see on my side.
I’m not sure that’s true, if you look up things like “tokens per kwh” or “tokens per second per watt” you’ll get results of people measuring their power usage while running specific models in specific hardware. This is mainly for consumer hardware since it’s people looking to run their own AI servers who are posting about it, but it sets an upper bound.
The AI providers are right lipped about how much energy they use for inference and how many tokens they complete per hour.
You can also infer a bit by doing things like looking up the power usage of a 4090, and then looking at the tokens per second perf someone is getting from a particular model on a 4090 (people love posting their token per second performance every time a new model comes out), and extrapolate that.
It’s literally the same thing, the obvious difference is how much usage it’s getting at a time per gpu, but everyone seems to assume all these data centers are running at full load at all times for some reason?
My guy, we’re not talking about just leaving a model loaded, we’re talking about actual usage in a cloud setting with far more GPUs and users involved.
Given that cloud providers are desperately trying to get more compute resources, but are limited by chip production - yes, of course? Why do you think they’re trying to expand their resources while their existing resources aren’t already limited?
Sure, and that’s why many cloud providers - even ones that don’t train their own models - are only slowly onboarding new customers onto bigger models. Sure. Makes total sense.
I compared the TDP of an average high-end graphics card with the GPUs required to run big LLMs. Do you disagree?
I do, because they’re not at full load the entire time it’s in use
They are, it’d be uneconomical not to use them fully the whole time. Look up how batching works.
I mean I literally run a local LLM, while the model sits in memory it’s really not using up a crazy amount of resources, I should hook up something to actually measure exactly how much it’s pulling vs just looking at htop/atop and guesstimating based on load TBF.
Vs when I play a game and the fans start blaring and it heats up and you can clearly see the usage increasing across various metrics
He isn’t talking about locally, he is talking about what it takes for the AI providers to provide the AI.
To say “it takes more energy during training” entirely depends on the load put on the inference servers, and the size of the inference server farm.
There’s no functional difference aside from usage and scale, which is my point.
I find it interesting that the only actual energy calculations I see from researchers is the training and the things going along with the training, rather then the usage per actual request after training.
People then conflate training energy costs to normal usage cost without data to back it up. I don’t have the data either but I do have what I can do/see on my side.
I’m not sure that’s true, if you look up things like “tokens per kwh” or “tokens per second per watt” you’ll get results of people measuring their power usage while running specific models in specific hardware. This is mainly for consumer hardware since it’s people looking to run their own AI servers who are posting about it, but it sets an upper bound.
The AI providers are right lipped about how much energy they use for inference and how many tokens they complete per hour.
You can also infer a bit by doing things like looking up the power usage of a 4090, and then looking at the tokens per second perf someone is getting from a particular model on a 4090 (people love posting their token per second performance every time a new model comes out), and extrapolate that.
One user vs a public service is apples to oranges and it’s actually hilarious you’re so willing to compare them.
It’s literally the same thing, the obvious difference is how much usage it’s getting at a time per gpu, but everyone seems to assume all these data centers are running at full load at all times for some reason?
My guy, we’re not talking about just leaving a model loaded, we’re talking about actual usage in a cloud setting with far more GPUs and users involved.
So you think they’re all at full load at all times? Does that seem reasonable to you?
Given that cloud providers are desperately trying to get more compute resources, but are limited by chip production - yes, of course? Why do you think they’re trying to expand their resources while their existing resources aren’t already limited?
Because they want the majority of the new chips for training models, not running the existing ones would be my assertion. Two different use cases
Sure, and that’s why many cloud providers - even ones that don’t train their own models - are only slowly onboarding new customers onto bigger models. Sure. Makes total sense.