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Artificial Intelligence (AI) is rapidly expanding, driven by a growing number of powerful computers. This expansion has placed significant strain on the data centers that run AI models. These facilities now consume more energy than ever before.
Modern AI models are larger and more complex, with some systems having billions of parameters derived from training data and running across thousands of computer chips. To keep up, companies have added more hardware, memory, and powerful networks. While this brute-force approach has propelled AI’s advancements, it also creates a new challenge: data centers are becoming energy-intensive.
Some tech firms are exploring alternative power sources for their data centers, including fossil fuels and nuclear plants. This trend is driven by the growing energy demand of AI models, which have spurred efforts to develop more efficient computer chips.
As a computer engineer at Georgia Tech specializing in high-performance computing, I see another path: making data centers more resource-aware and efficient.
### Energy and Heat
Modern AI data centers can use as much electricity as small cities. While the computing processes consume significant power, memory and cooling systems are also major contributors. As AI models grow, they need more storage and faster access to data, generating more heat. Additionally, powerful chips pose a central challenge in removing heat.
### A Smarter Way Forward
Addressing this requires rethinking system design and management for AI support. Here are three key ideas:
1. **Address Variability in Hardware**: Not all chips are the same. Differences in performance and energy efficiency must be recognized and adjusted accordingly.
2. **Adapt to Changing Conditions**: AI workloads vary over time, triggering changes like temperature fluctuations, power availability, and data traffic congestion. Systems should respond in real time.
3. **Break Down Silos**: Collaboration between chip designers, software engineers, and data center planners can lead to new energy-saving methods.
### Scaling with Intelligence
AI has the potential to transform various fields but faces risks related to performance, energy, and cost limitations. The future of AI depends not only on better models but also on better infrastructure.
To sustain AI growth for societal benefit, it’s crucial to shift from brute-force scaling to intelligent scaling.
