The unintended impact of AI
As AI scales, should there be more concerns about the environmental impact?
As AI scales, should there be more concerns about the environmental impact?
The recent frenzy surrounding the release of DeepSeek and how it may impact the market has opened discussion on the cost of AI from training costs (DeepSeek claimed their costs were $50 million compared to hundreds of millions for OpenAI GPT-4 and Google Gemini), hardware and energy costs, as well as open source savings.
With large-scale AI deployments requiring immense computational and storage resources, the environmental impact is an often-overlooked consequence. More and more organisations are looking to integrate AI in their front-end and back-end processes—be it Large Language Models (LLM) like GPT-4 and DeepSeek, Generative AI, Autonomous Systems, Robotics, or Computer Vision.
In lockstep with this uptake, demand for robust, scalable and efficient data centre infrastructure has soared through the roof.
As the AI trend marches on, companies will have to take active steps in mitigate the environmental impact of AI adoption in their data centers.
In this article, Tintri outline how eco-conscious storage and computing strategies will become necessary to keep pace with the requirement to maintain innovation and be mindful of their carbon footprint.
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