Red Hat and Intel spotlight scalable AI inference as enterprises move beyond the GPU gold rush

Red Hat and Intel discuss how scalable AI inference is reshaping enterprise infrastructure, with growing demand for balanced CPU-GPU deployments and more. UPDATED 09:52 EDT / MAY 13 2026 AI Red Hat and Intel spotlight scalable AI inference as enterprises move beyond the GPU gold rush by Ryan Stevens SHARE As companies move from testing AI to broader adoption, the biggest challenge is building scalable AI inference systems that perform without breaking the budget . The next wave of AI won't be won on raw power alone - it will be decided by who can do more with less.

When AI inference first took off, the focus was on deploying the largest possible models across massive GPU clusters following the rise of ChatGPT and open-weight models. That's when customers turned to Red Hat Inc. , looking for ways to scale those models across platforms like Red Hat Enterprise Linux and OpenShift without sacrificing control or cost efficiency, according to Taneem Ibrahim (pictured, right), director of engineering for AI inference at Red Hat.

"That's when the friction moment came in for us, like, 'How do I take this project - called vLLM, [which] we're the largest commercial contributor to - and work it at scale with a project like llm-d? "How you drive the cost per token down so that you can operationalize your AI, you can govern your AI [and] you can deploy it at scale? " Ibrahim and Bill Pearson (left), vice president of data center and AI at Intel Corp.