A Webinar for Platform Engineering, Kubernetes, and AI Infrastructure Teams

GPU sharing is quickly becoming a practical requirement for Kubernetes-based AI inference, as many modern workloads don’t need a full GPU to deliver value. But safely placing multiple containers on the same accelerator brings new challenges: scheduling, fairness, isolation, observability, and noisy-neighbor behavior.

Join us on August 6 and explore the GPU sharing landscape across Kubernetes: time-slicing, MPS, MIG, KAI Scheduler, and HAMi, and dives into the harder problem: operating shared GPUs in production, from tracking usage to enforcing fairness as demand shifts.

What you’ll take away:

• A clear view of the GPU sharing landscape and when to use each approach
• Practical patterns and pitfalls for running shared GPUs in production
• How automation can make GPU sharing more reliable over time

Can’t join us live? A recording of the session will be sent to registrants.