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Home > Data Center > 7 ways to prepare data center for AI’s power draw

7 ways to prepare data center for AI’s power draw

Artificial intelligence (AI) needs greater processor density, which augments the demand for cooling and raises power requirements.

Seda Nur Cinar by Seda Nur Cinar
November 27, 2019 11:47 pm
in Article, Data Center, Hardware
3 min read
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7 ways to prepare data center for AI’s power draw

A data center with typical enterprise applications, the average power consumption for a rack is around 7 kW. According to data center organization AFCOM, it’s common for Artificial Intelligence applications to use more than 30 kW per rack. So, AI needs much higher processor utilization, and the processors consume high energy. There are some options for minimizing energy consumption at the data center that utilizes special hardware for AI.

Table of Contents

  • Prefer liquid cooling
  • Go ECO mode if you can
  • Put AI cabinets on their own corner
  • Spread out your AI systems
  • Command the hot airflow
  • Move data less
  • Get newer hardware if you can

Prefer liquid cooling

In addition the spinning of the fan blades that consume energy, there is tremendous amounts of energy just to cool the whole volume of the data center. If data centers go liquid, they must deal less with the removing excess heat from the data center volume because of the placement of closed-loop liquid cooling takes away nearly all the heat from major heat-producing components like processors and compute cards and carries that out of the building to dissipate.

Go ECO mode if you can

You might want to go Eco-Mode with tour AI workloads if you are going with the already-available, non-AI-optimized data centers. You can limit the available raw power to the customer on a given data center or push them to the newer ones so that AI workloads run as optimized as possible. Since these workloads are scalable in sense of depth, you can also give the option to scale down the workload for the customer.

Put AI cabinets on their own corner

To optimize the power draw and with that heat dissipation in each area, you can segment AI workloads at more specialized parts or rooms of a data center. New data centers are not built with uniform power lines and cooling capabilities so try to move more power-hungry appliances like AI to specialized segments of the data center to provide them with the space to cool down.

Spread out your AI systems

In case of a uniform data center infrastructure, you may try to spread the cabinets with high power load and heat generation farther from each other to distribute them evenly on the floor or among racks. That would prevent areas with extreme heat release “hotspots” and increase the efficiency if you are employing a uniform cooling solution.

Command the hot airflow

Cold is the main theme of the whole space while there are rows of hot air aisles to collect two rows of servers’ hot air and draw them out of the data center. In this way, data center stays cool, drawn from the front sides of racks to pass through systems to be collected in the back corridor. Whereas it is a convenient approach it means trouble when a technician needs to work on the back side of a cabinet. But it is the more efficient way and it needs to be tested before any server spins up there.

Move data less

It is an energy-efficient approach not to carry data around even in a processor let alone in a data center. Do not spend time transferring data from one storage cluster to the system in a different cluster. Cluster them together if you can. That will help to converse the precious time and cause less downtime for high performance processors that you use on those precious rack spaces. Processes love data, keep them together.

Get newer hardware if you can

Every time AMD, Intel, Nvidia, or your other favorite hardware developer releases a new product, it will be more energy-efficient, meaning it will do more with less power and heat dissipation. You know when to upgrade your hardware, so go for newer components and up-to-date builds to converse energy and increase efficiency.

See more Data Center News


Tags: AMDArtificial IntelligenceIntelNvidia
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Seda Nur Cinar

Seda Nur Cinar

Editor of Cloud7, Seda is a Linux and opensource enthusiast, security researcher and a web application developer.

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