How to Measure the Carbon Footprint of Your Local Mistral-Poet LLM Running on Ollama with CodeCarbon
This video series explores explore the environmental impact of artificial intelligence and challenge the narrative that AI is inherently sustainable. I break down the real carbon footprint of popular ML models and provide actionable insights on measuring and reducing AI’s environmental impact.
Full Details:
You can read the article on my blog: The ‘Green’ AI Myth: Carbon Costs of ML Models & Insights on GEO and DeepSeek Fine-tuning
https://wp.me/p3Vuhl-3nO
The code is available on my github account:
https://shorturl.at/zLxZG
️Dive Deeper:
You can listen to the “podcast” extracted from this Blog Post Audio made with NotebookLM on this post:
https://on.soundcloud.com/PVzHx8TEb5b65HHg9
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Tag(s) : AI, AI-generated, artificial intelligence, environmental impact, environmental performance metrics, Generative Engine Optimization, KPIs, Learning-by-doing, model development, Streamlit, sustainability
Categorie(s) : Agile, AI, AI tools, Experiences, Python, Videos