What is AI inference, and why is it becoming so expensive to run? We break down how to optimize AI performance and ensure sustainability as models grow more complex. Every breakthrough in artificial intelligence relies on the moment a model delivers results to users. Join Sherard Griffin from the Red Hat OpenShift AI team as he explains how Red Hat AI Inference provides the control and flexibility needed to run any model, any accelerator, any cloud. Discover how open source runtimes like vLLM and Kubernetes-native frameworks like llm-d drive performance to squeeze every ounce of power from your accelerators and explore how model optimization and accuracy techniques that help you move toward a model-as-a-service approach. Transform your enterprise from a token consumer into an AI provider by orchestrating inference across your entire fleet. Learn more about how to optimize your inference across the hybrid cloud with Red Hat: 🚀 Explore Red Hat AI Inference → https://www.redhat.com/en/products/ai/inference-server 📖 Learn about AI inference → https://www.redhat.com/en/topics/ai/what-is-ai-inference 🛠️ Discover OpenShift AI → https://www.redhat.com/en/products/ai/openshift-ai #RedHat #vLLM #OpenShiftAI #EnterpriseAI

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