AI Has a Carbon Footprint, and It’s Time We Managed It
SEB Marketing Team
The buzz around Artificial Intelligence is exhilarating. From revolutionizing healthcare to powering the next generation of logistics, the potential feels limitless. But there’s a quiet cost lurking behind the brilliance: energy consumption. Specifically, the massive computational power required to train and run modern AI—especially Large Language Models (LLMs)—translates directly into a significant carbon footprint.
For any data scientist or tech leader, embracing sustainable AI isn’t just a corporate social responsibility talking point it’s the ethical baseline. The good news? There are concrete, actionable steps to drastically reduce this environmental impact.
We’re going to break down how to stop guessing about your model’s carbon cost and start building greener systems, from the silicon up.
Step 1: Don’t Guess—Quantify Your Model’s Impact
You can’t manage what you don’t measure. Before you can credibly claim a commitment to sustainability, you need to pin down the exact carbon cost of your AI lifecycle. The environmental impact is a direct function of two variables: how much energy you use and how dirty that energy is.
The Core Calculation
The total energy consumed across training, deployment, and inference is what matters.
- Total Energy Consumption: This comes down to tracking the specific hardware (GPUs/TPUs) power draw (Watts) multiplied by the job duration. Remember, while training is the intense sprint, the cumulative effect of inference—the millions of user queries over the model’s lifetime—can quickly become the marathon.
- Carbon Intensity Factor: This is the critical, location-dependent variable. A kWh of electricity consumed in a coal-heavy grid (e.g., some parts of Asia) has a much higher factor than one consumed in a hydro or solar-powered region (e.g., Nordic countries).
Action Item: Stop manually crunching these numbers. Use open-source tools like CodeCarbon or Carbontracker. They automatically monitor your computer usage and correlate it with regional grid data to give you a real-time, accurate footprint estimate.
Step 2: The Easiest Win—Infrastructure and Hardware Choices
The single most impactful decision often happens before you even write the first line of training code. Your hardware and data centre location matter immensely.
Location, Location, Location
Your cloud provider’s geographic region is a major lever. Don’t blindly pick the closest or cheapest region; pick the one with the greenest grid. Look for regions where the energy mix is dominated by wind, solar, or hydro power. By simply relocating your training job, you dramatically lower the factor in your calculation.
Performance Per Watt
Newer generations of accelerators (like GPUs or specialized AI chips) are engineered for higher performance per Watt. Prioritizing these chips over legacy hardware gives you a significant efficiency advantage from the start. A faster chip that finishes the job quicker can ultimately use less total energy, even if its peak power draw is higher.
Bonus Tip: Maximize Utilization. Idle compute draws power without delivering value. Implement smart scheduling to ensure your expensive, power-hungry clusters are either running jobs efficiently or are properly shut down.
Step 3: Train Smarter, Not Just Harder
The training phase is notorious for its power intensity. But many models waste computational cycles because of poor planning. You can shave off days of compute time—and the associated energy cost—by being a sharper engineer.
Implement Early Stopping
It sounds basic, but it’s often overlooked in the rush to completion. If your model’s performance on the validation set hasn’t improved over, say, five epochs, stop the training run. Letting it grind on “just in case” is burning energy for zero gain.
Embrace Mixed-Precision Training
This is a powerful, low-effort win. Most models default to using 32-bit floating point numbers (). By switching to lower-precision formats like or, you effectively halve the memory footprint and significantly speed up computations on compatible hardware. It translates to a faster, less energy-intensive training cycle without meaningful accuracy loss.
Step 4: Shrink the Model for the Long Run (Inference) 📏
Inference—the moment your trained model is actually used by customers—is where the carbon cost truly accrues over time. A smaller, faster model deployed across millions of queries is far more sustainable than a gigantic, inefficient one. This is the art of Model
By systematically applying these strategies, you ensure that the AI you deploy is not just intelligent but also lean, fast, and responsible. The future of technology demands that we be just as serious about our output as we are about our code’s output.