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Energy, carbon and water estimates for AI content, shown as ranges

Hacker News

Energy, carbon and water estimates for AI content, shown as ranges

Hi HN, I built this calculator because I noticed it's easy to know how much a LLM costs per token, but it's extremely difficult to know it's carbon footprint or power consumption. I created this as a reference based on some published papers and public information, and it helps to have a guide of the impact of photo and video generation using AI. I also added another factor about Token utilization, it's something that becomes important as the newer models start to use "thinking" modes to reach a conclusion. There is information up to earlier this year, so it's not possible to see the efficiency of newer models like OpenAI Sol or Kimi K3. All the calculator does is to provide estimations and translate them in ordinary figures so people can understand it. There is no login required or any other type of paywall. About the models, it's easier to make estimates for Open Source models (of course) so there's more confidence on that data. For the closed ones, there are estimations based on public available data. Please let me know if you have suggestions in how I can improve this calculator.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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