AMD and Cloudera stress tailoring infrastructure to business outcomes, not specs alone
As enterprises deploy AI systems, matching hardware choices to actual performance requirements—rather than chasing raw benchmarks—is becoming essential to controlling costs and delivering real business value.
Matching infrastructure to what a business actually needs is the starting point for cost-effective technology deployment. When companies bring AI workloads into production, financial constraints are forcing a harder look at how technical capabilities translate into measurable business impact. Raw performance metrics alone fail to show organizations what their spending will accomplish, according to Jeremy Girven, AWS technical enablement manager at Advanced Micro Devices Inc.
Is it acceptable for a customer, your end user, to wait 30 seconds for a response, or do they need it immediately? If they can wait 30 seconds, you can go with cheaper hardware. If they need it immediately, you're probably going to be on more cutting-edge hardware.
Jeremy Girven, AWS technical enablement manager at Advanced Micro Devices Inc.
Girven shared these insights during an exclusive conversation with theCUBE host Rebecca Knight at EVOLOVE26, part of SiliconANGLE Media's livestreaming coverage. The discussion centered on infrastructure sizing strategies, the partnership between AMD and Cloudera, and the financial realities of rolling out AI across enterprises.
Right-sizing infrastructure requires ongoing attention
The AMD and Cloudera partnership involves continuous testing of AMD hardware within Cloudera environments. Budget constraints sit at the heart of these infrastructure planning conversations, Girven noted.
I would say if you don't have the skill sets, you pull in a partner like Cloudera [or] partner with AMD, and we can help design the solution to meet the needs. We're not to the point where we have cookie-cutter solutions with AI yet … right now, it's kind of very bespoke for each customer.
Jeremy Girven
Infrastructure optimization does not end once systems go live. Organizations need to track how fully their resources are being used and recalibrate capacity on an ongoing basis, taking into account which locations handle their workloads, Girven explained.
https://www.youtube.com/embed/DHOu5XFVnec?feature=oembed
I would say definitely don't just focus on the technology of AI itself, but also keep in mind there's network fees and things like that that stack on top of it. I've seen many customers get bit by network egress fees that they didn't even calculate for.
Jeremy Girven
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