Real-world adoption stories from the CNCF ecosystem. Each case study highlights how organizations use HAMi to improve GPU utilization and scale AI infrastructure.
Scaling machine learning infrastructure with HAMi-based GPU virtualization on Kubernetes.
3x improvement in platform GPU utilization
Building a flexible GPU cloud with HAMi to increase utilization and improve delivery speed.
>80% average GPU utilization after vGPU adoption
Building a heterogeneous AI virtualization pooling solution (Effective GPU) with HAMi.
Up to 57% GPU savings for production and test clusters
Improving AI inference orchestration with HAMi in education-focused workloads.
90% of GPU infrastructure optimized using HAMi
