Sharing GPUs without Flying Blind: Kubernetes Patterns for AI Inference

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.

This 20 min session 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

Learn more and schedule a 1:1 demo at at https://www.kubex.ai/product/demo