Real-world adoption stories from the CNCF ecosystem. Each case study highlights how organizations use HAMi to improve GPU utilization and scale AI infrastructure.
Improving GPU utilization for autonomous driving workloads with HAMi-based GPU virtualization on Kubernetes.
10× GPU utilization improvement in CI pipelines
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
