3 posts tagged with "GPU Sharing"

The companion post HAMi-core adopted by NVIDIA KAI Scheduler already introduces KAI Scheduler and the collaboration behind this integration. This post skips that background and focuses on one question: when KAI Scheduler places two Pods on one GPU, does HAMi-core actually enforce each Pod's memory quota?

We verified the currently documented combination—KAI Scheduler v0.17.0 and kai-resource-isolator 1.1.0-chart—on GKE 1.35/COS/CDI. Both Pods shared the same NVIDIA T4, each saw a 4147 MiB ceiling, a 3 GiB CUDA allocation succeeded, and a cumulative 5 GiB allocation failed. The optional monitor also exported live limit and usage metrics for both Pods.

The integration target here is strictly HAMi-core, not the full HAMi platform. KAI Scheduler keeps its own scheduling capability and brings in HAMi-core to provide GPU memory isolation.

In June 2026, two core PRs were officially merged into the NVIDIA KAI Scheduler main branch. HAMi's GPU memory hard isolation shipped as a built-in feature starting with KAI Scheduler v0.16.4. Cloud-native GPU scheduling has officially moved from "cooperative sharing" into the "hard isolation" era.

What is HAMi?​

HAMi (Heterogeneous AI Computing Virtualization Middleware), formerly known as k8s-vGPU-scheduler, manages heterogeneous AI computing devices within Kubernetes clusters. It enables sharing of various AI devices while enforcing resource isolation between tasks, and provides a unified interface for different device types.