Generative AI tools like ChatGPT, BERT, LaMDA, and GPT-3 are making big waves in tech. Yet, there’s a hidden cost we often miss: their environmental impact. These powerful models have a high energy demand, resulting in significant carbon footprints. As AI use increases, so does our planet’s strain. Let’s explore this impact and how we can make AI greener.
The Energy Hungry Data Centers
Data centers, home to our valuable digital information, are responsible for 2-3% of global greenhouse gas emissions. The volume of data doubles every two years, requiring more energy to maintain. These systems consume around 7% of Denmark’s and 2.8% of the US’s electricity.
Prominent generative AI models run on GPUs, which use 10-15 times more energy than a traditional CPU. Amazon AWS, Google Cloud, and Microsoft Azure are currently leading the pack in the cloud provider race.
The Carbon Footprint of Generative AI
When calculating an AI model’s carbon footprint, we need to consider:
- Energy used in training the model
- Energy used running the model for inference
- Energy required to produce the needed computing hardware and cloud data center capabilities.
Training models, the most energy-demanding aspect of generative AI, results in considerable carbon emissions. For example, training a large deep learning model like GPT-4 uses approximately 300 tons of CO2.
Making AI Greener: How Can We Do It?
We can make generative AI more eco-friendly. Here are some strategies:
Use existing models: Creating and training AI models consume huge energy. So, instead of making new models from scratch, use already available ones.
Fine-tune existing models: Refining an existing model on your own content is much more energy-efficient than starting from scratch.
Utilize energy-conserving methods: Using less energy-demanding methods, such as TinyML, can drastically reduce energy consumption.
Only use large models when necessary: It’s not worth using a model that uses 3x more power for a small improvement in accuracy.
Choose your AI use wisely: Use AI tools for significant applications, not just for creating amusing stories or blog posts.
Choose green cloud providers: Some providers, like Google, are making strides in green energy use, which can lower your carbon footprint.
Re-use and recycle tech: Much like other resources, tech can be reused and recycled to lower carbon emissions.
Monitor your carbon footprint: Using tools like CodeCarbon, Green algorithms, and ML CO2 Impact can help keep track of your carbon emissions.
Our responsibility to the environment doesn’t stop at generative AI models. In everything we do, we need to consider our environmental impact and work towards more sustainable practices. We can’t debate the future of tech on a planet that’s no longer habitable.
Related Articles
