AI infrastructure consists of the hardware, compute resources, storage, and networking frameworks required to build, train, and deploy AI models.
Key takeaways
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Enterprise GPU utilization averages around 5%, making compute infrastructure the hardest cost to predict for production AI inference.
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Matching cloud hardware directly to workload demand prevents overprovisioning and drastically reduces compute spending.
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An AI adoption assessment framework helps infrastructure leaders benchmark their stack, avoid costly architectural re-dos, and scale AI workloads efficiently.
Video transcript
Video transcript
Are your GPUs doing any work or just sitting around looking busy? Enterprise GPU utilization can be shockingly low, as low as 5% in some cases, which means organizations are burning through cloud budgets and power without getting anywhere near the AI performance that they expected.
I'm Vineeth from Akamai Cloud, and in our August briefing, we've pulled together three resources to help your team change that figure.
First, watch our on-demand webinar called Right Compute, Right Time, Right Cost. It explores how to match the right infrastructure to the right workload without going over the top with over-provisioning everything.
Next, take our six-minute AI adoption challenge. In less time than it takes you to finish your morning espresso, you can benchmark your AI maturity, uncover potential gaps, and see where your organization should focus next.
Lastly, download the guide we pulled together called "Architecting a Path to AI Adoption." It's a practical guide to helping you build infrastructure that helps move AI out of experimentation and into production.
Now remember, the goal isn't simply to spend more on AI. It's to get more from every dollar, every watt of power, and every GPU. Come and explore the August briefing and check out all three resources today. And enjoy that coffee.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
AI infrastructure consists of the hardware, compute resources, storage, and networking frameworks required to build, train, and deploy AI models.
Enterprise teams can improve GPU utilization by matching hardware sizing directly to workload demand, employing dynamic scaling, and optimizing inference serving stacks.
An AI adoption assessment is a diagnostic tool that benchmarks an organization's infrastructure maturity against peer data to guide cloud architecture decisions.
Akamai Cloud provides distributed compute, low-latency GPU instances, and cloud storage optimized for high-performance AI inference workloads.