- llama-models/models/llama3_1/MODEL_CARD.md at main · meta-llama/llama-models
“Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.” - As Use of A.I. Soars, So Does the Energy and Water It Requires – Yale E360
Right now, it’s not possible to tell how your A.I. request for homework help or a picture of an astronaut riding a horse will affect carbon emissions or freshwater stocks. This is why 2024’s crop of “sustainable A.I.” proposals describe ways to get more information about A.I. impacts.In the absence of standards and regulations, tech companies have been reporting whatever they choose, however they choose, about their A.I. impact, says Shaolei Ren, an associate professor of electrical and computer engineering at UC Riverside, who has been studying the water costs of computation for the past decade. Working from calculations of annual use of water for cooling systems by Microsoft, Ren estimates that a person who engages in a session of questions and answers with GPT-3 (roughly 10 t0 50 responses) drives the consumption of a half-liter of fresh water. “It will vary by region, and with a bigger A.I., it could be more.” But a great deal remains unrevealed about the millions of gallons of water used to cool computers running A.I., he says.
The same is true of carbon.
“Data scientists today do not have easy or reliable access to measurements of [greenhouse gas impacts from A.I.], which precludes development of actionable tactics,” a group of 10 prominent researchers on A.I. impacts wrote in a 2022 conference paper. Since they presented their article, A.I. applications and users have proliferated, but the public is still in the dark about those data, says Jesse Dodge, a research scientist at the Allen Institute for Artificial Intelligence in Seattle, who was one of the paper’s coauthors.
- Projecting the Electricity Demand Growth of Generative AI Large Language Models in the US – Center on Global Energy Policy at Columbia University SIPA | CGEP %
Based on this approach, and in light of data from the EIA Annual Energy Outlook 2023,[13] this analysis suggests that by 2027 GPUs will constitute about 1.7 percent of the total electric capacity or 4 percent of the total projected electricity sales in the United States. While this might seem minimal, it constitutes a considerable growth rate over the next six years and a significant amount of energy that will need to be supplied to data centers. - Why AI Consumes So Much Energy and What Might Be Done About It – Kleinman Center for Energy Policy
So when you think about the figures that are out there, most people describe the data center as consuming anywhere from 1 to 2% of electricity that’s consumed globally. And of that, when you think about AI, they say roughly about 12% of that data center footprint today is occupied by AI-based workloads.So in this context, today the current footprint of AI is roughly about .1 to .2%, depending on the numbers you’re looking at. But I think the thoughts or interest is understanding how we continue to keep up with the demand for what’s happening in a lot of these AI development spaces — not just training, but also for inference — and how we make sure that we can not have the growth outpace our ability to offset the overall impact on the environment and the grid.
- Brian Roemmele on X: “You will own your own Personal AI and it could run on a: TI-84 calculator released in 2004. The amazing @andriy_mulyar and his team has done a miracle they have gpt4all (A light weight but effective clone of CharGPT-3.5 turbo) runnin
LLM on an old TI 84 . . . - I Made The Ultimate Cheating Device – YouTube
TI 84 as internet capable browserSomeone told me about a linux box on a calculator running an LLM. I haven’t found that yet but I did find this.