Look at alternatives, including AMD and cloud solutions, while staying mindful of how it all plays together. You may not be able to get Nvidia GPUs, but AWS, Azure, and Oracle Cloud have them, Kimball notes. Be strategic, perhaps by using cloud offerings to handle certain tuning or inference workloads, then bringing them back in-house when appropriate. “Have a better understanding of what absolutely has to be on prem and what can be in the cloud,” he says. That’s good advice, says Backblaze’s Thomas. When it comes to AI, think about performance tiers and the range of use cases you have. They don’t all need top-tier performance. “People get wrapped around axle of needing the top end. There’s a lot of flexibility in the edges, innovation in different hardware and software,” Thomas says. Gartner likewise advises companies to increase configuration flexibility and expand sourcing paths. That may include buying from secondary markets and lease-return programs to preserve continuity with existing infrastructure until the shortages pass, Forest says. Get started somewhere Even if you can’t acquire or have to wait for the infrastructure you need, don’t let that keep you from getting started with AI or other modernization projects. Options include public cloud and neocloud providers, Anderson says. WWT also provides capacity in its own lab so customers can get started with proof-of-concept projects. “Don’t just throw your hands up. We can help you find access to capacity,” Anderson says. “Production-scale AI may be delayed, but don’t let that derail your strategy.” Colocation providers may likewise be an option, especially if enterprises are struggling to acquire high-end networking equipment. Networking is a key value proposition for colocation providers, in that they have built-in connections to various cloud providers and other ecosystem players. Equinix, for example, has 280 data centers in 77 metropolitan
