As the demand for artificial intelligence (AI) continues to grow exponentially, it's putting a massive strain on the energy grid. The typical computing rack consumes around 20 kilowatt-hours per day, but a single rack based on Nvidia's GPUs can consume as much as 120 kilowatt-hours per day, equivalent to the power consumption of 4.5 US homes.
To mitigate this issue, experts suggest focusing on making AI more efficient. Incorporating data products, such as feature stores, can reduce time to market and add trust to AI and machine learning use cases. Building AI infrastructure with reusable code assets can also lead to long-term sustainability.
Establishing a robust framework with standards and protocols can ensure safety, consistency, and efficiency while scaling data and AI. Leveraging open-source models, such as Meta's Llama, can create custom-built models and reduce power consumption. Additionally, developing more efficient hardware, such as SambaNova's chip, can offer 10 times the performance of GPU-based solutions while consuming about one-tenth the power.
By adopting these strategies, we can scale AI without breaking the grid, ensuring a more sustainable and efficient future for AI development. This is crucial, as the demand for AI is only expected to continue growing, and finding ways to make it more efficient is essential for mitigating its environmental impact.
