Despite the incredible focus by the press on this topic, Mistral's lifecycle emissions in 18 months were less than the typical annual emissions of a single A320neo in commercial service.
Fossil fuel companies are damn good at PR, and they know well that they simply can't make themselves look good. The next best thing? Make someone else look worse.
If an Average Joe hears "a company that hurts the environment" and thinks OpenAI and not British Petroleum, that's a PR win.
1Kg of Beef costs:
- The energy equivalent of 60.000 ChatGPT queries.
- The water equivalent of 50.000.000 ChatGPT queries.
- The water equivalent of 18.8 Tons of Beef.
- The CO2 equivalent of 204 Tons of Beef.
and one large LLM per 6 months.
So yeah, maybe use ChatGPT to ask for vegan recipes.
People will try to blame everything else they can get a hold on before changing the stuff that really has an impact, if it means touching their lifestyle.
The LLMs are not the problem here.
If you buy $10 in tokens, that probably folds into ~$3 to $5 dollars in electricity.
Which would be around 30 to 90 kWhr in electricity.
Depending on the source, it could be anywhere from ~500g/kWhr (for natural gas) and ~24g/kWhr for hydroelectric.
It's a really wide spread, but I'd say for $10 in tokens, you'd probably be in the neighbourhood of 1 kg to 40 kg of emissions.
What's a good thing is that a lot of the spread comes from the electricity source. So if we can get all of these datacenters on clean energy sources it could change emissions by over an order of magnitude compared to gas turbines (like XAi uses).
I don't think the cost of the ai is close to converging to the price of power yet. Right now its mostly the price of hardware and data center space minus subsidies.
People are selling AI at a loss right now.
A very small model could run on device to automatically switch and choose the right model based on the request. It would help navigate the difficult naming of each model of each vendor for sure.
This is harder than it looks. A “router” model often has to be quite large to maintain routing accuracy, especially if you’re trying to understand regular user requests.
Small on-device models gating more powerful models most likely just leads to mis-routes.
If we take the total training footprint and divide that by the number of tokens the model is expected to produce over its lifetime, how does that compare to the marginal operational footprint?
My napkin math says per token water and material footprints are up 6-600% and 4-400% higher respectively for tokens on the order of 40B to 400M.
I don't have a good baseline on how many tokens Mistral Large 2 will infer over the course of its lifetime, however. Any ideas?
Even if the company is "green" they make money, they pay employees/stockholders, those people use the money to buy more things and go on vacations in airplanes. Worse, they invest the money to make more money and consume more goods.
Even your gains and vegetables are shipped in to feed you, if you walk to the grocery store. You pay rent/mortgage for a house built with concrete and steel. The highest priced items you pay for are also likely the most energy and environmentally costly. They create GDP.
It's a little weird with LLMs right now, because everything is subsidized by VC, Ads, BigCo investment so you can't see real costs. They're probably higher than the $30-200/mo you pay, but they're not 10x the price like your rent, car payment, food, vacation, investment/pension are.
So I guess one saves a lot of emissions if one stops tiktok-ing, hulu-ing, instagram reel-ing, etc.