Adopt On-Premises AI at Your Own Pace

After two to three years of experimenting with artificial intelligence in the cloud, many organizations are now looking to bring their AI infrastructure in-house.

As AI environments grow, recurring operating expenditures have also soared to unsustainable levels for many companies, causing business and IT leaders to look for a less expensive option. Security and data governance concerns are also driving some of the momentum toward on-premises AI environments, as are worries about vendor lock-in. However, GPUs remain both costly and scarce, making them an impractical choice for many organizations.

Fortunately, most AI environments don’t actually require GPUs. Although the hardware has become almost synonymous with AI, they are mostly only necessary for organizations that are training AI models or running extremely heavy workloads. We estimate that only 10% to 20% of our customers actually need GPUs to power their AI environments. For the rest, it’s much more economical to leverage CPU-based infrastructure such as Intel Xeon processors for inference workloads and only scale up when needs demand it.

Red Hat OpenShift AI provides a unified environment to run AI workloads alongside containers and virtual machines, giving organizations the flexibility to scale their AI initiatives at their own pace. Teams can start small, deploying inference workloads on existing Xeon infrastructure, then seamlessly integrate accelerators or expand into hybrid cloud environments as their needs grow. As demands increase, organizations might opt for Intel’s Gaudi AI accelerators, which can power larger enterprise AI models with high precision while still offering attractive pricing compared with most GPU options.

For many organizations, the move toward OpenShift AI also coincides with a broader shift away from legacy virtualization platforms. Rising licensing costs have prompted some IT teams to evaluate alternatives to their existing virtualization environments, with many looking to solutions that can support both traditional workloads and emerging AI use cases. Red Hat OpenShift AI is a great choice for these teams, as it allows organizations to consolidate virtual machines, containers and AI workloads onto a single platform, setting the stage for long-term modernization while maintaining business continuity.

As teams re-evaluate both their virtualization environments and their AI infrastructure needs, CDW can help design, deploy and optimize solutions that meet current and anticipated needs. Whether an organization is modernizing its legacy virtualization environment, piloting small language models on Xeon or scaling into a hybrid cloud AI environment with Gaudi accelerators, our experts offer the knowledge and services to get projects to the finish line and provide ongoing support.

We’re exiting the initial wave of AI hype, when organizations were happy to outbid each other for top-shelf, AI-ready hardware — even if they didn’t have a compelling business case to justify the expense. Business and IT leaders are quickly beginning to demand that their AI initiatives show ROI, and this means putting their money where the use cases are rather than where the hardware is.

By starting small with infrastructure that meets their current needs, organizations can avoid overspending now and give themselves the freedom to quickly scale up when new needs arise.