Introducing Project Navigator: From AI intent to optimized deployment on Red Hat OpenShift AI (opens in new tab)

You've picked a model. Maybe it's a 70 billion parameter large model because someone on the team saw it top a leaderboard. Now you need it running in production on your Red Hat OpenShift AI cluster. So you start tuning batch sizes, figuring out quantization, sizing GPU requests, writing Kubernetes manifests, and hoping the out of memory errors stop before your deadline hits.We've watched this play out enough times to see the pattern. The hard part of enterprise AI isn't just picking a model, ...