AI and Power Consumption

With AI (also known as Artificial Intelligence) gaining traction and becoming increasingly common in our daily lives, you may often wonder how it all works. Contrary to belief and to what you would see in science fiction, our modern-day AI models are considered tools that are comprised of algorithms and data that are developed and fine tuned in what would be termed “machine learning”. This is important to note, as we evaluate the general power needs that AI requires in order to function. AI is generally considered an energy-intensive resource in order to operate.

With most currently available AI models available, there are generally 2 factors that separate tasks and functions handled by AI. The first being what is considered the “machine learning” function that we mentioned earlier. This could be considered “training”, in which the AI is actively “learning” and being programmed on how to operate. The second part is what’s known as “inference”. Inference refers to the process in which users enter prompts to live AI models in order to receive data, information, or other responses. This also pertains to users generating images, videos, or other types of AI driven media. While both functions are considered extremely energy intensive, the “machine learning” process is known to have a higher power consumption rate when compared to the “inference” process.

How Is AI Currently Being Powered?

A majority of AI models available in the market are set up in data centers on remote servers. That means that as the AI models are being built out and refined, they are using energy tied to the data center’s electrical utility and other resources in order to operate. These resources could include network data and cooling capacities that most data centers utilize in order to keep their servers running efficiently. According to the International Energy Agency, data centers account for 1 to 1.5% of total global electricity use. If we factor in the advent, implementation, and continued development of AI – these energy consumption numbers can skyrocket.

What Part of AI is Consuming So Much Energy?

As we mentioned before, there are generally 2 functions of AI that both consume large amounts of energy. That would be the “machine learning” function and the “inference” function. While this is true when looking at the base power needs of general AI models, there are additional factors that play a role in increased power consumption. For example, larger, more powerful models such as ChatGPT may be programmed with an extremely large data set and parameters that enable it to effectively perform across many different countries with a large input of users. Essentially, the greater the AI model is scaled up, the greater the need for power.

What Do Backup Generators Have to Do With AI?

As we mentioned, AI models are being hosted in data center locations across the globe. This is important because we need to consider a few factors in relation to data centers themselves. Data centers are categorized into four different tiers that all have different standards.

Tier 1 data centers are considered extremely basic. Tier 1 data centers generally don’t include any measures of redundancy and can experience up to 28.8 hours of downtime per year.

Tier 2 data centers are designed with limited redundancy in mind, which is to cover overall power and cooling needs. Tier 2 data centers can experience up to 22 hours of downtime per year.

Tier 3 data centers are considered to be much more reliable than previous tiers, as they generally experience 1.6 hours of downtime per year. This is because Tier 3 data centers are designed with greater redundancy in mind and used by large businesses that cannot afford downtime in order to operate.

Lastly, we have Tier 4 data centers, which are known to be the best of the best. Tier 4 data centers may experience only minutes of downtime per year, rating at about 26.3 minutes or less. This is due to being fully redundant thanks to the utilization of backup generators and depending on the setup, integrated UPS systems that can help prevent prolonged downtime due to power outages.

Backup generators play a vital role in ensuring that data centers can continue operating even when power to the main electrical grid is lost. While Tier 4 data centers are considered to be on the highest end of data center infrastructure, backup generators can be installed and utilized by the lower tier data centers to ensure some level of redundancy and ensure minimal downtime.

What Are Power Consumption Expectations with AI?

As AI continues to gain more traction and become an everyday tool for many users, the demand for much more powerful, intricate AI models may continue to increase. Considering the current landscape of AI and power consumption, as the demand increases for AI features and functions continue, the greater the power needs will be as they are directly correlated to one another.

While there are many considerations to be made when installing a backup generator for your data center to help host and power AI models, this is a general overview of power requirements needed for AI to properly operate and function. If you have any questions, are looking for a commercial generator for your data center, please reach out to us at Woodstock Power Company!

Give us a call or send us an email: 610-658-3242 or sales@woodstockpower.com

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THE WOODSTOCK POWER COMPANY EXPERIENCE

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