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.
Te 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.
During the use of HAMi, it is common for Pods to be created and remain in a Pending state, particularly due to the following two issues:
This section provides a rough walkthrough of the related code to explain the interactions between components during scheduling and how resources are calculated. Other details may be omitted.
HAMi (Heterogeneous AI Computing Virtualization Middleware), formerly known as k8s-vGPU-scheduler, is an innovative solution designed to manage heterogeneous AI computing devices within Kubernetes clusters. This all-in-one middleware enables the sharing of various AI devices while ensuring resource isolation among different tasks. By improving the utilization rates of heterogeneous computing devices, HAMi provides a unified multiplexing interface that caters to diverse device types.
