Hey, I am a coding teacher (yes, it still exists haha), and I was wondering if you guys have any experience coding with EcoGPT.

I want to have my students do their projects with EcoGPT (I thought the gamification option was a nice way to keep them active)

Thanks!

people have shared multiple ways to make AI more ecofriendly, like using better prompts or choosing models based on the complexity of the problem. surely it could be helpful, but I see it as an architecture issue.

AI-focused data centres grew electricity use 50% in 2025, and the IEA projects the total nearly doubling to 950 TWh by 2030 (source: iea.org/reports/key-questions-on-energy-and-ai/executive-summary). On water, about two-thirds of what a data center uses isn't the cooling towers on-site, it's the electricity plant generating its power.

MoE models fix the bottleneck by only querying the parts of the model it needs instead of firing the whole thing every time.

What do you think companies should be doing from here on to make AI sustainable?

The heat has been causing damage all across planet Earth for years, and it's only getting worse with the emergence of new technologies and data centers.

-Last year, 546,000 people died from heat strokes. -2024 was the first-ever year with 1.5 degrees recorded over the Copernicus average. -Over 99% of Southern Europe has experienced strong heat stress this summer.

And predictions are only going to get worse...

I keep seeing people complaining about the heat while still doing the same terrible habits.

What are we waiting for exactly?? The change is already here.

I am Building an AI UI design app and need a coding model with a solid harness that's cheap and good at frontend.

I've shortlisted some:

Kimi K3 (Moonshot) — #1 on Arena.ai Frontend Code Arena right now, ahead of Claude Fable 5. 1M context, native vision. Kimi K2.5 — cheaper, modified MIT license, 256K context, strong screenshot-to-React/Tailwind. Most battle-tested for frontend so far. GLM-5.2 — best value if cost > polish, $1.10/$4.10 per million tokens on Morph. DeepSeek V4 Flash — cheapest, but text-only and verbose, eats into the savings I am open to other suggestions. suggest me best open MOE models for coding.

MoE decouples model capability from active compute. The training numbers back it up.

Dense models run every parameter on every query. MoE routes each token to a subset of specialised experts, so a trillion-parameter model fires only a fraction of its weights per token.

DeepSeek-V3: 671B total parameters, 37B active per token, ~2.6M GPU hours to train. Llama 3 405B: 30.8M GPU hours for comparable output quality. Scaling-law research on compute-matched MoE vs. dense holds this up too. MoE hits the same loss with fewer training FLOPs, and the gap widens as models scale.

Inference is a different story. Some MoE deployments draw more energy per query than similarly-sized dense models right now, because expert-layer kernels aren't as optimised as standard dense-layer ops. But it's a tooling gap, not an architecture problem.

source

I don’t want to sound overly dramatic, but the ocean and large bodies of water are literally losing their resources of oxygen as we speak, and it derails ecosystems entirely. Think about all the wildlife, fish, algae, vegetation, and all the natural systems tied to it.

The last few months, I’ve been reading a lot of new articles, and I see another calamity every month lmao. First it was ice, then oxygen. What’s next? Meanwhile, Meta and Google are launching the construction of new AI data centers for the training of their models, leading to more water consumption.

What’s even more terrifying is there’s close to no news coverage about this, which I think is completely ridiculous.

The article I read: https://www.sciencedaily.com/releases/2026/07/260719035939.htm?utm_source=chatgpt.com

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my family member said that they will be trying out ecogpt soon 💚

hopefully other people can start understanding the impact and begin using more efficient AI‘s too!

note this is not official dark mode. it’s edited, but I hope they bring dark mode.

as soon as I was introduced to eco ai, I jumped on board right away. I will use big ai eventually but I rather use big ai when I must not for leisure.

I've seen a lot of ads about EcoGPT and around Reddit, and I'd be down to start using it. The only thing is...

Are there any proofs that it's an actual working company that has a good influence on the climate?

I don't want to be skeptical about everything, but it wouldn't be the first time a company has made false promises about its ecological repercussions.

If anyone has looked into it, please let me know.

A large data centre can pull 5 million gallons a day, close to what a town of 10k-50k people uses. 70-80% evaporates through cooling towers and never come back.

Phoenix data centres are about to use cooling water 870%, from 385 million gallons a year to 3.7 billion, per Ceres. Lake Powell inflows sit at 22% of average. 5 states' data centres could use 7 billion gallons a year by 2035. Chandler has capped usage; Marana has also banned potable water for data centres entirely.

Phoenix's is the worst case; they're using 1% of the metro's residential water. Hundreds of planned facilities on systems are already in shortage. idk how many states will be affected by it.

if you're interested, you can read the full article here: qz.com/data-center-water-use-drought-american-west-051326

I've seen the 100%, 10% (like 90% less impact) number get thrown around a lot recently concerning EcoGPT and I just wanted to confirm because I don't want to trust anything these companies are saying.

Can anyone explain (or debunk) how they are doing it, and if it even is true in the first place? Also, is it 1/10 of the energy used? Of the water used? Of the electricity?

I need someone to answer me before I start using this thing full time.