As LLMs become part of our daily workflows, it’s time to ask a critical question:
What’s the environmental cost of our AI usage?
In this episode, I explore the energy footprint of LLMs and how we can observe and estimate their impact using open-source tools like:
OpenLLMetry – Distributed tracing for LLMs
OpenLit – GPU usage, cost estimation & evaluation
Ecologits – Estimating energy, GHG emissions, and resource depletion
CodeCarbon – Real energy tracking for self-hosted models
As always, I’ve prepared a GitHub repo with all the examples and code used in the episode:
GitHub - isItObservable/ecologits
Whether you’re running hosted models or self-hosting your own, this episode will help you observe your AI workloads responsibly and understand their environmental impact.
Watch the full episode here:
https://www.youtube.com/watch?v=D-sLBzggFkQ&feature=youtu.be
Let’s build smarter — and greener — AI systems.
hasztag#Observability hasztag#LLM hasztag#Sustainability hasztag#OpenTelemetry hasztag#AI hasztag#GreenTech hasztag#Ecologits hasztag#OpenLLMetry hasztag#OpenLit hasztag#CodeCarbon