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, it's the stretch between "this model looks good" and "this model is serving traffic reliably." That str
Enterprise organizations are pushing past initial AI experimentation, shifting priorities from testing isolated models to safely deploying governable, production-ready workflows across the open hybrid cloud. Managing this transition requires an infrastructure strategy that balances rapid automation and platform innovation with a rock-solid security posture that safeguards data perimeters against emerging threats. Check out this curated roundup of the top cross-portfolio posts our readers are exploring right now. The content spans from groundbreaking command-line AI assistants and quantum-resis