Fast and simple AI deployment on Intel Xeon with Red Hat OpenShift

Fast and simple AI deployment on Intel Xeon with Red Hat OpenShift Why AI on Xeon? Intel Xeon’s hardware features Xeon use case 1: AI inference Xeon use case 2: RAG and secure data processing Xeon use case 3: Agentic AI Xeon on OpenShift: What’s new Features vLLM CPU image AI quickstarts Next steps Red Hat AI About the author Alex Sin More like this AI insights with actionable automation accelerate the journey to autonomous networks Cracking the inference code: 3 proven strategies for high-performance AI Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share AI adoption and development have accelerated with generative and agentic AI reaching the masses. As new markets emerge, businesses have been struggling to take advantage of AI for real returns on investment. Although GPUs have dominated the infrastructure, increasing costs and decreasing availability due to demand have prompted leaders to seek alternatives that still meet performance requirements and customer satisfaction standards. Meanwhile, the developers and engineers working on AI face challenges in complex and time-consuming infrastructure setup and difficulty in building out software stacks and architectures for optimal large language model (LLM) inference with retrieval augmented generation (RAG). Ease of use, security of proprietary data, and even how to get started building AI are technical challenges that may bar developers from entry to AI. The collaboration between Intel and Red Hat combines the performance of Xeon CPUs with the scalability of Red Hat OpenShift AI, offering a protected and flexible foundation for deploying agentic AI in the enterprise. On this platform, customers can build AI and machine learning models and applications more securely at scale across hybrid cloud environments. To simplify the adoption process, Intel has created a number of AI quickstarts. AI quickstarts are examples of real-world business use cases that can quickly be deployed on Xeon with OpenShift, accelerating development and time-to-market. These quickstarts are available through the AI quickstarts catalog. Although GPUs have dominated deep learning, generative AI, and agentic AI, inference can use smaller and more cost-effective computing platforms to meet functional and performance requirements. CPUs have historically been the platform of choice for data processing, data analytics, and classical machine learning.