HAMi has always offered per-Pod GPU scheduling policies through the hami.io/gpu-scheduler-policy annotation: binpack to pack workloads onto as few cards as possible, spread to distribute them, mutex (new in v2.10.0) to demand an exclusive card. Until now, the annotation accepted exactly one value.
Real clusters rarely want just one behavior at a time. A typical production wish list looks like this: pack inference replicas tightly to leave whole cards free, but keep each Pod's GPUs on the same NUMA node for bandwidth, and give the latency-critical tier cards of its own. That is three policies in a single sentence. Before v2.10.0 you had to pick one and give up the rest.
v2.10.0 closes this gap: hami.io/gpu-scheduler-policy now accepts an ordered, comma-separated list of policies, so filter-style and sort-style policies compose (#2621, @mesutoezdil, closes #2010). This post explains how the combination actually works, and how to adopt and verify it. If you prefer to learn by doing, the companion Lab 14: Composable GPU Scheduling Policies on GKE walks through every scenario below on a real cluster.
From September 7 to 9, KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China 2026 will take place at the Shanghai International Convention Center. This will be HAMi's first KubeCon China appearance since the project was accepted as a CNCF Incubating project in July.
The HAMi community is bringing a keynote appearance, two technical sessions, and a project booth to the show: from a 5-minute lightning talk on dynamic MIG partitioning, to a production story of GPU virtualization at thousand-GPU scale. If you are attending, come say hi.
The HAMi community is proud to announce the official release of HAMi v2.10.0. This release advances HAMi on three fronts: more flexible scheduling policies, broader heterogeneous accelerator coverage, and a richer 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.
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 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.
KCD Beijing 2026 was one of the largest Kubernetes community events in recent years.
Over 1,000 people registered, setting a new record for KCD Beijing.
The HAMi community not only gave a technical talk but also set up a booth, engaging deeply with developers and enterprise users from the cloud-native and AI infrastructure fields.
The topic of this talk was:
From Device Plugin to DRA: GPU Scheduling Paradigm Upgrade and HAMi-DRA Practice
This article combines the on-site presentation and slides for a more complete technical review. Slides download: GitHub - HAMi-DRA KCD Beijing 2026.
