8 ways to prepare your data center for AI’s power draw

Artificial intelligence requires greater processor density, which increases the demand for cooling and raises power requirements.

2 data center servers

As artificial intelligence takes off in enterprise settings, so will data center power usage. AI is many things, but power efficient is not one of them.

For data centers running typical enterprise applications, the average power consumption for a rack is around 7 kW. Yet it’s common for AI applications to use more than 30 kW per rack, according to data center organization AFCOM. That’s because AI requires much higher processor utilization, and the processors – especially GPUs – are power hungry. Nvidia GPUs, for example, may run several orders of magnitude faster than a CPU, but they also consume twice as much power per chip. Complicating the issue is that many data centers are already power constrained.

Cooling is also an issue: AI-oriented servers require greater processor density, which means more chips crammed into the box, and they all run very hot. Greater density, along with higher utilization, increases the demand for cooling as compared to a typical back-office server. Higher cooling requirements in turn raise power demands. 

So what can you do if you want to embrace AI for competitive reasons but the power capacity of your existing facility isn’t up to the high-density infrastructure requirements of AI? Here are some options.

Consider liquid cooling

Fan cooling typically loses viability once a rack exceeds 15 kW. Water, however, has 3,000 times the heat capacity of air, according to CoolIT Systems, a maker of enterprise liquid cooling products. As a result, server cabinet makers have been adding liquid pipes to their cabinets and connecting water piping to their heat sinks instead of fans.

“Liquid cooling is definitely a very good option for higher density loads,” says John Sasser, senior vice president for data center operations at Sabey, a developer and operator of data centers. “That removes the messy airflow issue. Water removes a lot more heat than air does, and you can direct it through pipes. A lot of HPC [high performance computing] is done with liquid cooling.”

Most data centers are set up for air cooling, so liquid cooling will require a capital investment, “but that might be a much more sensible solution for these efforts, especially if a company decides to move in the direction [of AI],” Sasser says.

Run AI workloads at lower resolutions

Existing data centers might be able to handle AI computational workloads but in a reduced fashion, says Steve Conway, senior research vice president for Hyperion Research. Many, if not most, workloads can be operated at half or quarter precision rather than 64-bit double precision.

To continue reading this article register now