The models were still (and probably still are actively being) trained in giant data centres that devour energy and water.
Sure, you using it technically doesn’t hurt the environment any more but the technology as such definitely does.
Training AI models takes quite a bit of energy. But probably less than you think. Without knowing how much you think I can’t say for sure, but I can say that AI is so far down the list of water consumers that any time it comes up I can be fairly confident.
Without knowing for sure we should not be giving data centers from giant corporations the benefit of the doubt.
30 million gallons of water stolen noticed by the entire towns water pressure dropping seems rationally low to not have triggered a review of their systems?
They chose small towns and desert areas because they can be bullied and lobbied for cheaper for the investors, not because its for the good of the community.
The models were still (and probably still are actively being) trained in giant data centres that devour energy and water.
I hate to tell you, but your Lemmy instance is in a datacenter too. Most everything on the Internet is in a datacenter.
The important detail is that an upscaling model and a large language model are vastly different things despite both containing the word model and using neural networks.
As to training costs. You can train an upscaling model on your phone in a few hours. It would take you millions of years to train an LLM on your phone (not to mention requiring about 12,000x as much RAM).
You’re just repeating talking points on topics without understanding them or how they apply.
I do not have a problem with upscaling models and DLSS in general (up to ver. 4.5) is perfectly fine.
However, DLSS 5 is not just upscaling anymore, it’s fully analyzing and generatively replacing parts of the image (lighting, material properties, …)
And image based gen AI definitely cannot be trained on a phone, even less so than LLMs.
So you’re either not fully informed about DLSS 5 or arguing in bad faith.
The point is addressing the cost of training the DLSS5 model.
The ‘models trained in giant data centers that devour energy and water’ are LLMs. DLSS5 isn’t an LLM, nor does it have anywhere near the training costs. DLSS5’s training costs are a rounding error when compared to LLMs.
However, DLSS 5 is not just upscaling anymore, it’s fully analyzing and generatively replacing parts of the image (lighting, material properties, …)
I know what Neural Rendering is. Calling it upscaling or generative upscaling doesn’t change my point about training.
And image based gen AI definitely cannot be trained on a phone, even less so than LLMs.
I know some CS professors that would be surprised to learn this.
I’m looking at the result of a lab where I completed training on 4x4 MLP denoiser on a TI-Nspire CX II… a graphing calculator with 64MB RAM and a 369MHZ processor. About 300k FLOPS/step, takes about an hour.
Yes, training a larger model on a phone would take longer but as long as a reasonable sized chunk of the model could fit into RAM it can be trained, eventually.
They’re not trained on phones as a rule because you can throw a lot more compute at them with dedicated hardware. You can train an LLM on an abacus if you’re patient enough.
The models were still (and probably still are actively being) trained in giant data centres that devour energy and water.
Sure, you using it technically doesn’t hurt the environment any more but the technology as such definitely does.
Training AI models takes quite a bit of energy. But probably less than you think. Without knowing how much you think I can’t say for sure, but I can say that AI is so far down the list of water consumers that any time it comes up I can be fairly confident.
Without knowing for sure we should not be giving data centers from giant corporations the benefit of the doubt.
30 million gallons of water stolen noticed by the entire towns water pressure dropping seems rationally low to not have triggered a review of their systems?
https://www.politico.com/news/2026/05/08/georgia-data-centers-water-00909988
They chose small towns and desert areas because they can be bullied and lobbied for cheaper for the investors, not because its for the good of the community.
DLSS models are not even in the same league as LLMs from a training perspective.
The comment section is full of people trying to use anti-LLM arguments against DLSS because they both use neural networks.
It’s like people are against matches because they’re hot and nuclear weapons are hot so matches are basically nuclear weapons.
Yeah. It’s beyond unnuanced thinking and is a bit brain-dead :(
See AI, neuron activates, “AI bad”
I hate to tell you, but your Lemmy instance is in a datacenter too. Most everything on the Internet is in a datacenter.
The important detail is that an upscaling model and a large language model are vastly different things despite both containing the word model and using neural networks.
As to training costs. You can train an upscaling model on your phone in a few hours. It would take you millions of years to train an LLM on your phone (not to mention requiring about 12,000x as much RAM).
You’re just repeating talking points on topics without understanding them or how they apply.
I do not have a problem with upscaling models and DLSS in general (up to ver. 4.5) is perfectly fine.
However, DLSS 5 is not just upscaling anymore, it’s fully analyzing and generatively replacing parts of the image (lighting, material properties, …) And image based gen AI definitely cannot be trained on a phone, even less so than LLMs.
So you’re either not fully informed about DLSS 5 or arguing in bad faith.
The point is addressing the cost of training the DLSS5 model.
The ‘models trained in giant data centers that devour energy and water’ are LLMs. DLSS5 isn’t an LLM, nor does it have anywhere near the training costs. DLSS5’s training costs are a rounding error when compared to LLMs.
I know what Neural Rendering is. Calling it upscaling or generative upscaling doesn’t change my point about training.
I know some CS professors that would be surprised to learn this.
I’m looking at the result of a lab where I completed training on 4x4 MLP denoiser on a TI-Nspire CX II… a graphing calculator with 64MB RAM and a 369MHZ processor. About 300k FLOPS/step, takes about an hour.
Yes, training a larger model on a phone would take longer but as long as a reasonable sized chunk of the model could fit into RAM it can be trained, eventually.
They’re not trained on phones as a rule because you can throw a lot more compute at them with dedicated hardware. You can train an LLM on an abacus if you’re patient enough.