It’s becoming painfully obvious that there is no way to ethically use frontier models powered by these monstrosities. It is currently 100 F in Tuckahoe, the largest city in Henrico County… and they’re asking people to not use electricity so that these heat-and-pollution-generating slop factories can use it instead.
It is currently 100 F in Tuckahoe, the largest city in Henrico County… and they’re asking people to not use electricity so that these heat-and-pollution-generating slop factories can use it instead.
In other words, this shit is killing people? I don’t think many houses in the US are made so that you can survive 38°C outside without A/C ?
Oh, there are definitely ethical ways to use these models. It’s just that those methods are not being enforced by local counties or your governments. Thus, companies are able to do whatever the hell they want, which means it’s going to be unethical by default.
What we need is regulation, enforcement, and a stop letting these companies trample all over everything they want to.
No, there really isn’t. The frontier models are created through massive plagiarism. They’re designed to be addictive to use. They consume massive amounts of resources to feed you slop. They are inherently unethical. We’re burning the planet down to keep them running, and we don’t even have a demonstrable financial ROI to show for it.
Stop using them. If your employer makes you use them, maliciously comply by wasting tokens until the financial pain is too great for them to bear and they stop. If you yourself are addicted, switch to small, local, open-source, open-weight models you can run yourself. You won’t burn the world down running a small model on your own computer.
You have that backwards. The only thing you gain from running local models is privacy. It is not cheaper, it is not more efficient. You are actively hurting the environment MORE by using a local model on your own. LLM efficiency sky rockets the more users there are on a single loaded model.
IMO the only way we get to efficient LLM usage would be by having very efficient non frontier models running only for its local community to use, where you can have assurances on whether its power source is clean or not. That doesn’t help with the plagiarism aspect though
Local model: Spends most of its time turned off. Only active when I want it to be active, and only for a little while. Dedicated solely to generating the small amounts of code I use it for. Does nothing else. Costs $0 per token, and electricity costs are negligible.
Frontier model: Always on, running on millions of GPUs. Would be burning down the planet even if hardly anyone was using it. Incredibly wasteful, being used for trivial tasks and convincing people that their horrible ideas are visionary every day. Misspelling “strawberry” for the masses. Trained specifically to be addictive. Can easily cost a software developer who is addicted to AI thousands of dollars a month, with the recent price increases.
I’d love to see some data to back up the assertion that frontier models are somehow cheaper and more efficient than running a model locally.
You’re probably burning more energy turning it off and on again. It doesn’t really use any noticeable power sitting idle.
Anyway, a direct comparison would be pretty difficult because your model is probably tens of billions of parameters, not over a trillion. Energy consumption per output token will probably be a bit higher for the frontier models but something that people have found is that higher quality models often need fewer tokens to achieve the same goal. Plus how many times do you re-prompt your local model vs Claude Fable or Opus for example to get the desired result?
You’re probably burning more energy turning it off and on again. It doesn’t really use any noticeable power sitting idle.
I am absolutely not burning more energy than a frontier model by doing things like putting my laptop to sleep or shutting down unused services when I want to conserve battery power.
Anyway, a direct comparison would be pretty difficult because your model is probably tens of billions of parameters, not over a trillion.
True.
Energy consumption per output token will probably be a bit higher for the frontier models but something that people have found is that higher quality models often need fewer tokens to achieve the same goal.
That’s actually not true. In fact it’s much the opposite. Frontier models churn through tokens at a much higher rate, because of their higher complexity and higher number of parameters. Research is still new on this, but having a frontier model analyze your code files versus a small, local model for the same task seems to be enormously wasteful. If you must use a frontier model for something, have it do that work after receiving the output from an agent using a small model to read and summarize your code.
Plus how many times do you re-prompt your local model vs Claude Fable or Opus for example to get the desired result?
…Almost never? I’m not a fan of letting AI do much of ANY of my coding, because it will inevitably bloat my codebase with garbage regardless of which model I use. So I severely restrict my model usage to simple, clearly-defined, narrow-scoped tasks that can save me a bit of time, and that’s it. With guardrails and discipline like that, I barely ever have the need to re-prompt.
Very serious. Your personal amount of usage means nothing at all in this conversation.
It is entirely about tokens per watt. The amount of energy the memory operations involve scale incredibly well when people are accessing the same object in memory simultaneously. Last I looked it was around a 10x difference for the same models efficiency.
If you want me to be your personal search engine you’ll need to wait a bit, im making dinner right now and would rather look for the articles on my desktop.
Very serious. Your personal amount of usage means nothing at all in this conversation. It is entirely about tokens per watt. The amount of energy the memory operations involve scale incredibly well when people are accessing the same object in memory simultaneously. Last I looked it was around a 10x difference for the same models efficiency.
Hold up. Are you talking about caching? Because if you are… yeah. That has nothing to do with the model and everything to do with the service layer around the model. The same service layers can be - and have been - implemented in tools like Lemonade Server, llama.cpp, Ollama, etc.
And I really do want to know your sources.
Mine say GPT 5.5 is probably using quite a lot more than 0.34 Wh per query (0.34 Wh is what Sam Altman claimed for the then-current version of GPT in June of 2025, but he hasn’t released numbers since then and no one has done an independent analysis). With Claude, an independent estimate from last year pegged Sonnet at 0.8 Wh for a short prompt, 2.8 Wh for a medium one, and 5.5 Wh for a long one. Current numbers are, again, almost certainly much higher. And just for fun, there’s DeepSeek (which I’ve never used and never would use), with the reasoning-tuned DeepSeek-R1 hitting a whopping 29 Wh for a complex query.
Meanwhile, small, open models are probably in the 0.07 - 0.2 range, depending on the model, the hardware it’s running on, and the nature of the query. Of course, there are much weightier open models too, with ones like Llama 3.1 405B using about 9 Wh for a medium-length prompt. On the other hand… who is going to run that on their local machine?
Look… If I’m wrong, and using local models the way I do - sparingly and infrequently - really does consume more electricity than using Claude Code, I want to know. I have no problem whatsoever with eschewing AI models entirely, since I despise all of them. But given how tight-lipped OpenAI and Anthropic are about energy consumption per average prompt, and what independent analyses have estimated, I am highly skeptical that they are acting as some sort of paragons of environmental stewardship.
Not talking about caching (though there would be some decent memory savings due to that on general platforms like ChatGPT and tools like Codex). I am talking about large batch sizes, which are concurrent requests all accessing the same memory at the same time. The model is loaded once onto the GPU(s) and then many simultaneous requests can read that memory at the same time. When those requests are all processing their responses simultaneously, the energy per token drops off a cliff.
And yes, running a smaller model would generally take less power, but thats not really a fair comparison. Small models just wont give you the same results as larger ones. You need to compare it apples to apples. If you want to compare your local Qwen model running on your laptop, you compare those numbers to larger systems supplying that same qwen model to thousands of people. Just because we are comparing cloud services to local doesn’t automatically mean GPT 5.6 vs Qwen 3.6 27B. There are plenty of cloud AI providers running all sorts of models and sizes.
As for one of the articles I learned alot of this from originally, this is one I recommend going through. It really goes deep into the whole topic: https://arxiv.org/html/2601.22076v1
It’s becoming painfully obvious that there is no way to ethically use frontier models powered by these monstrosities. It is currently 100 F in Tuckahoe, the largest city in Henrico County… and they’re asking people to not use electricity so that these heat-and-pollution-generating slop factories can use it instead.
This is insanity.
In other words, this shit is killing people? I don’t think many houses in the US are made so that you can survive 38°C outside without A/C ?
This administration has killed a lot of folks.
The only reasonable response to these requests is fuck you figure out your data center power issues yourself.
Power is for the people.
They are being paid. Why else would they doom their own people? You won’t stop it if you don’t stop them.
Oh, there are definitely ethical ways to use these models. It’s just that those methods are not being enforced by local counties or your governments. Thus, companies are able to do whatever the hell they want, which means it’s going to be unethical by default.
What we need is regulation, enforcement, and a stop letting these companies trample all over everything they want to.
No, there really isn’t. The frontier models are created through massive plagiarism. They’re designed to be addictive to use. They consume massive amounts of resources to feed you slop. They are inherently unethical. We’re burning the planet down to keep them running, and we don’t even have a demonstrable financial ROI to show for it.
Stop using them. If your employer makes you use them, maliciously comply by wasting tokens until the financial pain is too great for them to bear and they stop. If you yourself are addicted, switch to small, local, open-source, open-weight models you can run yourself. You won’t burn the world down running a small model on your own computer.
You have that backwards. The only thing you gain from running local models is privacy. It is not cheaper, it is not more efficient. You are actively hurting the environment MORE by using a local model on your own. LLM efficiency sky rockets the more users there are on a single loaded model.
IMO the only way we get to efficient LLM usage would be by having very efficient non frontier models running only for its local community to use, where you can have assurances on whether its power source is clean or not. That doesn’t help with the plagiarism aspect though
Are you serious?
I’d love to see some data to back up the assertion that frontier models are somehow cheaper and more efficient than running a model locally.
You’re probably burning more energy turning it off and on again. It doesn’t really use any noticeable power sitting idle.
Anyway, a direct comparison would be pretty difficult because your model is probably tens of billions of parameters, not over a trillion. Energy consumption per output token will probably be a bit higher for the frontier models but something that people have found is that higher quality models often need fewer tokens to achieve the same goal. Plus how many times do you re-prompt your local model vs Claude Fable or Opus for example to get the desired result?
I am absolutely not burning more energy than a frontier model by doing things like putting my laptop to sleep or shutting down unused services when I want to conserve battery power.
True.
That’s actually not true. In fact it’s much the opposite. Frontier models churn through tokens at a much higher rate, because of their higher complexity and higher number of parameters. Research is still new on this, but having a frontier model analyze your code files versus a small, local model for the same task seems to be enormously wasteful. If you must use a frontier model for something, have it do that work after receiving the output from an agent using a small model to read and summarize your code.
…Almost never? I’m not a fan of letting AI do much of ANY of my coding, because it will inevitably bloat my codebase with garbage regardless of which model I use. So I severely restrict my model usage to simple, clearly-defined, narrow-scoped tasks that can save me a bit of time, and that’s it. With guardrails and discipline like that, I barely ever have the need to re-prompt.
Very serious. Your personal amount of usage means nothing at all in this conversation. It is entirely about tokens per watt. The amount of energy the memory operations involve scale incredibly well when people are accessing the same object in memory simultaneously. Last I looked it was around a 10x difference for the same models efficiency.
If you want me to be your personal search engine you’ll need to wait a bit, im making dinner right now and would rather look for the articles on my desktop.
Hold up. Are you talking about caching? Because if you are… yeah. That has nothing to do with the model and everything to do with the service layer around the model. The same service layers can be - and have been - implemented in tools like Lemonade Server, llama.cpp, Ollama, etc.
And I really do want to know your sources.
Mine say GPT 5.5 is probably using quite a lot more than 0.34 Wh per query (0.34 Wh is what Sam Altman claimed for the then-current version of GPT in June of 2025, but he hasn’t released numbers since then and no one has done an independent analysis). With Claude, an independent estimate from last year pegged Sonnet at 0.8 Wh for a short prompt, 2.8 Wh for a medium one, and 5.5 Wh for a long one. Current numbers are, again, almost certainly much higher. And just for fun, there’s DeepSeek (which I’ve never used and never would use), with the reasoning-tuned DeepSeek-R1 hitting a whopping 29 Wh for a complex query.
Meanwhile, small, open models are probably in the 0.07 - 0.2 range, depending on the model, the hardware it’s running on, and the nature of the query. Of course, there are much weightier open models too, with ones like Llama 3.1 405B using about 9 Wh for a medium-length prompt. On the other hand… who is going to run that on their local machine?
Look… If I’m wrong, and using local models the way I do - sparingly and infrequently - really does consume more electricity than using Claude Code, I want to know. I have no problem whatsoever with eschewing AI models entirely, since I despise all of them. But given how tight-lipped OpenAI and Anthropic are about energy consumption per average prompt, and what independent analyses have estimated, I am highly skeptical that they are acting as some sort of paragons of environmental stewardship.
Not talking about caching (though there would be some decent memory savings due to that on general platforms like ChatGPT and tools like Codex). I am talking about large batch sizes, which are concurrent requests all accessing the same memory at the same time. The model is loaded once onto the GPU(s) and then many simultaneous requests can read that memory at the same time. When those requests are all processing their responses simultaneously, the energy per token drops off a cliff.
And yes, running a smaller model would generally take less power, but thats not really a fair comparison. Small models just wont give you the same results as larger ones. You need to compare it apples to apples. If you want to compare your local Qwen model running on your laptop, you compare those numbers to larger systems supplying that same qwen model to thousands of people. Just because we are comparing cloud services to local doesn’t automatically mean GPT 5.6 vs Qwen 3.6 27B. There are plenty of cloud AI providers running all sorts of models and sizes.
As for one of the articles I learned alot of this from originally, this is one I recommend going through. It really goes deep into the whole topic: https://arxiv.org/html/2601.22076v1