Best Sustainable Practices for AI Development

AI Doesn’t Float! Data Centers are anchored in physical infrastructure with real-world trade-offs.   In our collective pursuit of AI acceleration, one question often goes unasked: Where will all the power come from and who will bear the cost of delivering it? While headlines warn that data center emissions may hit 3–4% of global CO₂ by 2030, the IEA ( https://lnkd.in/eKe89HjZ) estimates this number closer to 1%. So why the wide gap? Because location, timing, and grid readiness matter more than averages. In places like Dublin, data centers already consume nearly 20% of available power. In the U.S., data centers may surpass the electricity demand of all domestic heavy industry combined by 2030. This is a planning crisis already underway. Through my recent advisory work, I’ve seen how forward-thinking infrastructure planning, decarbonization strategy, and locational modeling can either unlock or bottleneck entire regions. Data center clusters need grid flexibility, clean firm power, and local engagement. However, very often we see a race to build without a strategy to share or sustain.   The IEA’s new report (“Energy and AI” https://lnkd.in/eKe89HjZ) lays out three imperatives: 1. Diversify clean power supply (yes, including geothermal, SMRs, and batteries). 2. Accelerate grid build-out, not just generation. 3. Foster better collaboration between tech and energy planners, upstream, not after permits are filed.   A sustainable AI future is about cross stakeholder coordination not solely carbon emissions. Cities, utilities, and communities deserve a seat at the table before another megawatt is claimed.     #EnergyTransition #DataCenters #GridPlanning #SustainableAI #IEA #Infrastructure #ClimateTech #Resilience

#SustainableAI :: Understanding #AI's full #climate impact means looking past model training to real-world usage, but developers can take tangible steps to improve efficiency and monitor #emissions -- writes Lev Craig on TechTarget. Building more sustainable AI will require a multifaceted approach that encompasses everything from model architecture to underlying infrastructure to how AI is ultimately applied. A 2019 paper estimated that training a big transformer model on GPUs using neural architecture search produced around 313 tons of carbon dioxide emissions, equivalent to the amount of emissions from the electricity that 55 American homes use over the course of a year. It remains difficult to accurately estimate the environmental impact of AI. But although obtaining accurate sustainability metrics is important, developers of machine learning models and AI-driven software can make more energy-efficient design choices even without knowing specific emissions figures. For example, eliminating unnecessary calculations in model training can reduce overall emissions. Incorporating sustainability monitoring tools can help technical teams better understand the environmental impact of their models and the applications where those models are used. For example, the Python package CodeCarbon and open source tool Cloud Carbon Footprint offer developers snapshots of a program or workload's estimated carbon emissions. Similarly, IBM - Red Hat's #Kepler tool for #Kubernetes [https://lnkd.in/gr_yzdAW] aims to help DevOps and IT teams manage the energy consumption of Kubernetes clusters. As regulations evolve, taking a sustainability-first approach will set companies up for success compared with simply playing whack-a-mole with each new policy that comes out. Thus, putting in the upfront effort to build more sustainable AI systems could help avoid the need to make costly changes to infrastructure and processes down the line. #Sustainability #carbonfootprint #netzero #ESG Sandro Mazziotta :: Vincent Caldeira https://lnkd.in/gjNF3EUk

Nobody talks about this, but AI is actually destroying the environment. (A single AI model can emit more carbon than 5 cars do over their lifetimes.) The amount of energy and CO2 released to train AI models is concerning. The GPUs used for AI training are significantly more power-hungry than regular CPUs.  For instance, training the GPT-3 model consumed as much energy as powering 126 single-family homes for a year. The more I get into AI, the more I realize the environmental costs involved. Another example? The University of Massachusetts pointed out that it takes more than 626,000 pounds of CO2 to train a generic neural network. Good news is:  There are solutions.  Huge companies like Microsoft, IBM, and Google are taking steps to mitigate AI's environmental impact. → Microsoft  Aims to power all data centers with 100% renewable energy and is working on ways to return energy to the grid during high demand. → IBM Is focusing on "recycling" AI models and make them more efficient over time rather than training new ones from scratch. → Google Cloud  Is optimizing data center operations by using liquid cooling and ensuring high utilization rates to minimize energy waste.  I love AI, but we can’t pretend these issues don’t exist. I’m glad to see that big companies are taking a step towards mitigating the risks, but there’s still a long way to go. A sustainable future isn’t possible without sustainable AI. P.S. I have a whole article on the environmental impacts of AI published in Forbes, link in the comments.

The expansive growth of #AI has brought about an imminent challenge: its considerable energy demands. For reference, consider MIT Technology Review's observation: one AI model's training equates to the carbon footprint of five American cars, spanning their entire lifecycle. Insights: ➡ AI's Power Requirement: Models such as ChatGPT utilize GPUs intensively, resulting in massive electricity and cooling needs. ➡ #Blockchain's Potential: Networks, exemplified by CUDOS, are focusing on distributed cloud computing, distributing AI tasks across multiple data centers. Impressively, a vast portion of these centers is now reliant on renewable energy sources. ➡ Achieving #Efficiency & #Sustainability: The distributed model not only decentralizes computing tasks but ensures fewer energy-concentrated spots, offering a resilient platform for AI's growth without escalating energy costs. ➡ Strategic Synergy: The convergence of AI and blockchain offers a blueprint for scalable, sustainable technological advancement, addressing the dual challenges of AI's energy hunger and the overarching cloud computing carbon trail. The intersection of AI and blockchain signals a future where #innovation aligns with environmental #responsibility. https://lnkd.in/epxridCx