How to Prepare Your Rack Server for AI

Data center power consumption will increase as artificial intelligence becomes more prevalent in business settings. AI is many things, but it’s not energy-efficient.

The typical power consumption for a rack in data centers running common enterprise applications is roughly 7 kW. However, data center organization AFCOM claims that it is typical for AI applications to consume more power than 30 kW per rack. That’s because AI demands a lot more processing power, and processors—especially GPUs—are power-hungry.

So what can you do if your current facility’s power capacity is insufficient to meet the high-density infrastructure requirements of AI but you want to use AI for competitive reasons? The following choices.

1: Implement Liquid Cooling

Once a rack server hits 15 kW, fan cooling often becomes uneconomical. However, according to CoolIT Systems, a manufacturer of industrial liquid cooling products, water contains 3,000 times the heat capacity of air.

As a result, manufacturers of server cabinets have begun including liquid pipes in their products and using water piping to link heat sinks to cabinets rather than fans.

“Liquid cooling is unquestionably an excellent choice for greater density loads. That fixes the clogged airflow problem. Compared to air, water can be directed through pipes and can remove a lot more heat.

2: Load Balancing

AI workload load balancing refers to the equitable distribution of jobs across the CPUs and GPUs that are currently available on the server. The server may effectively utilize all hardware, limiting overloading of particular parts and lowering power spikes, by avoiding focusing tasks on specific components.

This strategy contributes to a more effective and sustainable environment for data centers by optimizing power usage, enhancing performance and ensuring steady operation.

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3: High Performance GPU

Your rack server must be equipped with strong GPUs or specialized AI accelerators to handle the computationally intensive tasks demanded by AI workloads. This server’s overall power draw will be greatly reduced thanks to the hardware components’ effective processing of large volumes of data and sophisticated AI algorithms.

By shifting these specialized accelerators’ heavy lifting to them, the effort placed on the CPU is decreased, resulting in optimized power usage and enhanced performance.

Due to the rack server’s ability to handle AI workloads well and manage power requirements effectively for high-performance and sustainable computing, AI applications can now reach their full potential.

4: Use a Cloud-Based Platform

There are several benefits to cloud-based AI systems over conventional on-premises AI infrastructure. One of their biggest benefits is that they can aid in lowering your data center’s overall power consumption.

This is because cloud-based AI platforms are frequently housed in sizable, incredibly effective data center that are built to maximize energy efficiency. Cloud-based AI platforms can also be scaled up or down as necessary, which can further cut down on power usage.

You might scale down your cloud-based AI platform during certain hours if, for instance, you only need to execute AI workloads occasionally. This will aid in energy conservation and lower your total expenses.

5: Evaluate the Power Budget

Add up the power ratings of all parts, including CPUs, GPUs, storage devices, networking equipment, and other peripherals, to determine the total power budget for your rack server. Verify the power specifications provided by the manufacturer of each component.

To support peak loads and future expansions, make sure the power supply has a wattage rating higher than the overall power draw. To avoid overloading the power supply, which may result in system instability, decreased performance, or even hardware damage, accurate power budgeting is essential.

6: Monitor Power Usage

In environments powered by AI, it is essential to regularly check power usage utilizing server management tools. Administrators can locate power-intensive components or processes that might cause inefficiencies by analyzing power utilization during AI workloads.

This information enables optimization techniques to achieve more effective power utilization, such as load balancing or modifying power management settings.

The rack server performs as efficiently as possible thanks to proactive power management, supporting green computing methods, and maximizing the advantages of AI workloads.

7: Build an AI Containment Segment

A portion of your data center that is designated solely for AI workloads is known as an AI containment segment. The infrastructure and specialized cooling systems that are often found in this area can help to increase the productivity of your AI tasks.

You can lessen the total power drain of your data center by separating your AI workloads into a containment segment. This is because AI workloads tend to be quite power-intensive, and by isolating them in a containment segment, you can make sure they are not vying for resources with other workloads.

In The Nutshell

These procedures will help you correctly set up your rack server for the power demands of AI applications, assuring peak performance and dependability while minimising energy use. The right preparation will enable your server to fully realise AI’s promise and support the expansion and success of your organisation in the era of data-driven decision-making.

Omar Borkan