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.
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.
Next week, HAMi will be featured in multiple activities at KubeCon + CloudNativeCon Europe 2026, including Project Pavilion booth, technical sessions, main stage demo, and post-conference AI-related events.
As a CNCF Sandbox project, HAMi focuses on GPU virtualization, sharing, and scheduling, which is increasingly intersecting with AI infrastructure topics in the Kubernetes ecosystem. KubeCon + CloudNativeCon Europe 2026 will be held in Amsterdam from March 23-26, with March 23 as pre-event programming and March 24-26 as the main conference.
The HAMi community is proud to announce the official release of HAMi v2.8.0. This represents a milestone version in terms of architectural completeness, scheduling reliability, and ecosystem alignment.
v2.8.0 not only introduces multiple key feature updates but also delivers systematic enhancements in Kubernetes native standard alignment, heterogeneous device support, production readiness, and observability, making HAMi more suitable for AI production clusters that require long-term operation with high stability and clear evolution paths.
This article provides a detailed overview of the major updates in v2.8.0.
