15 posts tagged with "Kubernetes"

The HAMi community is proud to announce the official release of HAMi v2.10.0. This release advances HAMi on three fronts: richer scheduling policies, broader heterogeneous accelerator coverage, and a deeper scheduler ecosystem.

v2.10.0 introduces dynamic Flexible MIG, a new mutex scheduling policy, a long-requested NUMA-aware sort fix, composable scheduler policies, gang-scheduling (PodGroup) support, and correct init-container resource accounting. On the device side it adds AMD MI300X and Biren support, heterogeneous Ascend management that lets template-based vNPU and HAMi-core nodes coexist in one cluster, and vNPU HAMi-core monitoring. It also debuts a KAI Scheduler + HAMi-core integration through the new KAI Resource Isolator companion project.

This article walks through the major updates in v2.10.0.

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.

KubeCon + CloudNativeCon Japan 2026 was held July 28-30 at Pacifico Yokohama. HAMi took part with a main-stage session and a booth in the Project Pavilion. Reza Jelveh presented the session and staffed the booth.

For the full overview, talk slides, and the SNOW case study, see the KubeCon Japan 2026 page.

Held on June 18-19, 2026, in Mumbai, India, KubeCon + CloudNativeCon India 2026 brought together cloud native practitioners, platform engineers, AI infrastructure teams, and open source contributors from across the ecosystem. As AI emerged as one of the conference's defining themes, HAMi showcased how Kubernetes-native GPU sharing helps organizations maximize accelerator utilization while maintaining workload isolation and operational flexibility.

From the opening keynote to live booth demonstrations and technical discussions with engineering teams, the event highlighted a growing industry focus: making expensive GPU infrastructure practical for multi-tenant AI workloads.

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.

HAMi is coming to KubeCon + CloudNativeCon India 2026, June 18–19 in Mumbai. After a strong showing at KubeCon Europe 2026, HAMi community contributors and CNCF Ambassadors from India are bringing GPU sharing demos and conversations to Mumbai.

Source: mesutoezdil.substack.com
GitHub Repo: kagentWithHami
Chinese translation by Jimmy Song, originally published on WeChat

One physical NVIDIA L40S virtualized into 10 vGPUs with HAMi. An AI Agent deployed as a Kubernetes CRD via kagent. Agent-to-Agent delegation, GPU pod creation, overcommit protection - all driven by Llama 3.3 70B with no closed-source dependencies.

The HAMi community is proud to announce the official release of HAMi v2.9.0. This represents a milestone version in terms of heterogeneous device virtualization depth, scheduler ecosystem expansion, and Kubernetes native standards alignment.

v2.9.0 introduces the Ascend 910C HAMi-core mode, HAMi-DRA general availability, and Volcano vGPU upgrade to v0.19, along with systematic enhancements in observability, security, and stability. This release also welcomes 19 new contributors for the first time.

This article provides a detailed overview of the major updates in v2.9.0.

Managing GPU resources in Kubernetes has long been a "blind spot" for operators. You know GPUs are being used, but answering questions like "which node has idle capacity?", "is this workload actually utilizing its allocated GPU?", or "what is the overall cluster utilization trend?" often requires piecing together kubectl get, Prometheus PromQL, and log output.

Today, the HAMi community is introducing HAMi WebUI - an open-source GPU monitoring dashboard that puts your entire GPU cluster into a single, visual interface.

HAMi WebUI v1.1.0 is now available as the first official major release.

Together with the core HAMi scheduler, WebUI completes the full loop: from GPU scheduling to visual observability.

The recently concluded KubeCon + CloudNativeCon Europe 2026 sent an increasingly clear signal to the industry:

Cloud native is rapidly evolving from an "application runtime platform" into the operational foundation for AI infrastructure.