How to help your customers invest in AI infrastructure

The right AI infrastructure can help your customers turn data into actionable information. But building and scaling that infrastructure can be challenging. Find out why—and how you can make it easier. 

  • April 24, 2023 | Author: Peter Krass
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Get smarter about helping your customers create an infrastructure for AI systems that leverage their data into actionable information.

A new Supermicro white paper, Investing in AI Infrastructure, shows you how.

As the paper points out, creating an AI infrastructure is far from easy.

For one, there’s the risk of underinvesting. Market watcher IDC estimates that AI will soon represent 10% to 15% of the typical organization’s total IT infrastructure. Organizations that fall short here could also fall short on delivering critical information to the business.

Sure, your customers could use cloud-based AI to test and ramp up. But cloud costs can rise fast. As The Wall Street Journal recently reported, some CIOs have even established internal teams to oversee and control their cloud spending. That makes on-prem AI data center a viable option.

“Every time you run a job on the cloud, you’re paying for it,” says Ashish Nadkarni, general manager of infrastructure systems, platforms and technologies at IDC. “Whereas on-premises, once you buy the infrastructure components, you can run applications multiple times.”

Some of those cloud costs come from data-transfer fees. First, data needs to be entered into a cloud-based AI system; this is known as ingress. And once the AI’s work is done, you’ll want to transfer the new data somewhere else for storage or additional processing, a process of egress.

Cloud providers typically charge 5 to 20 cents per gigabyte of egress. For casual users, that may be no big deal. But for an enterprise using massive amounts of AI data, it can add up quickly.

4 questions to get started

But before your customer can build an on-prem infrastructure, they’ll need to first determine their AI needs. You can help by gathering all stakeholders and asking 4 big questions:

  • What are the business challenges we’re trying to solve?
  • Which AI capabilities and capacities can deliver the solutions we’ll need?
  • What type of AI training will we need to deliver the right insights from your data?
  • What software will we need?

Keep your customer’s context in mind, too. That might include their industry. After all, a retailer has different needs than a manufacturer. But it could include their current technology. A company with extensive edge computing has different data needs than does one without edge devices.

“It’s a matter of finding the right configuration that delivers optimal performance for the workloads,” says Michael McNerney, VP of marketing and network security at Supermicro.

Help often needed

One example of an application-optimized system for AI training is the Supermicro AS-8125GS-TNHR, which is powered by dual AMD EPYC 9004 Series processors. Another option are the Supermicro Universal GPU systems, which support AMD’s Instinct MI250 accelerators.

The system’s modularized architecture helps standardize AI infrastructure design for scalability and power efficiency despite complex workloads and workflow requirements enterprises have, such as AI, data analytics, visualization, simulation and digital twins.

Accelerators work with traditional CPUs to enable greater computing power, yet without slowing the system. They can also shave milliseconds off AI computations. While that may not sound like much, over time those milliseconds “add up to seconds, minutes, hours and days,” says Matt Kimball, a senior analyst at Moor Insights & Strategy.

Roll with partner power

To scale AI across an enterprise, you and your customers will likely need partners. Scaling workloads for critical tasks isn’t easy.

For one, there’s the challenge of getting the right memory, storage and networking capabilities to meet the new high-performance demands. For another, there’s the challenge of finding enough physical space, then providing the necessary electric power and cooling.

Tech suppliers including Supermicro are standing by to offer you agile, customizable and scalable AI architectures.

Learn more from the new Supermicro white paper: Investing in AI Infrastructure.


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