Boris Gamazaychikov
bgamazay
AI & ML interests
Sustainable AI
Recent Activity
upvoted
an
article
1 day ago
We Got Claude to Fine-Tune an Open Source LLM
updated
a Space
3 days ago
AIEnergyScore/Leaderboard
published
an
article
4 days ago
AI Energy Score v2: Refreshed Leaderboard, now with Reasoning π§
Organizations
upvoted
an
article
1 day ago
Article
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350
published
an
article
4 days ago
Article
AI Energy Score v2: Refreshed Leaderboard, now with Reasoning π§
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commented on
β‘ Power, Heat, and Intelligence βοΈ - AI Data Centers Explained π
28 days ago
Yes, an interesting time for Scope 2 for sure. My understanding, though, is that that update is focused on defining how Market Based is calculated (and what types of mechanisms are allowable). I have always been a proponent of focusing on Location Based first, and these updates/debates underscore that even more. While there is a need to encourage impactful renewables investment through Market Based accounting, Location Based should be the focus when trying to quantify actual, physical emissions.
upvoted
an
article
28 days ago
Article
β‘ Power, Heat, and Intelligence βοΈ - AI Data Centers Explained π
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published
an
article
about 1 month ago
Article
β‘ Power, Heat, and Intelligence βοΈ - AI Data Centers Explained π
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15
upvoted
an
article
about 1 month ago
Article
βοΈ When we pay for AI cloud compute, what are we really paying for? π²
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upvoted
an
article
about 2 months ago
Article
π What kind of environmental impacts are AI companies disclosing? (And can we compare them?) π
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published
an
article
8 months ago
Article
AI Models Hiding Their Energy Footprint? Hereβs What You Can Do
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upvoted
an
article
8 months ago
Article
Are AI Agents Sustainable? It depends
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reacted to
fdaudens's
post with β€οΈ
10 months ago
Post
2712
βοΈ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.
You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.
Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.
166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.
Why this matters:
- Teams can pick efficient models that still get the job done
- Developers can optimize for energy use from day one
- Organizations can finally predict their AI environmental impact
If you're building with AI at any scale, definitely worth checking out.
π leaderboard: https://lnkd.in/esrSxetj
π blog post: https://lnkd.in/eFJvzHi8
Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg
You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.
Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.
166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.
Why this matters:
- Teams can pick efficient models that still get the job done
- Developers can optimize for energy use from day one
- Organizations can finally predict their AI environmental impact
If you're building with AI at any scale, definitely worth checking out.
π leaderboard: https://lnkd.in/esrSxetj
π blog post: https://lnkd.in/eFJvzHi8
Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg