The Linux Foundation LFX Mentorship Program 2026 Term 3 is live, and HAMi is mentoring four open-source projects from September to November 2026.
Mentee applications open August 3, 2026 and close August 18, 2026. Whether your interest is low-level C/C++ performance, GPU observability, container isolation security, or developer education, there is a project for you.
KubeCon + CloudNativeCon Japan 2026 was held July 28-30 at the 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.
On July 16, 2026, Li Mengxuan, Co-founder & CTO of Dynamia and HAMi author, delivered a technical talk on vLLM deployment and compute optimization at vLLM Meetup. Built around one pointed question, "Are you making good use of your compute?", the talk laid out a complete evolution path for vLLM inference clusters, from "getting it to run" to "squeezing the hardware dry", broken down into three clear stages.
This recap walks through the talk slide by slide, combining the deck with the on-site Q&A notes.
We are excited to announce that on July 2, 2026, HAMi was accepted as a CNCF Incubating project, with the CNCF Technical Oversight Committee passing the incubation vote unanimously in favor.
This is an important milestone following HAMi joining the CNCF as a Sandbox project in August 2024. It means the CNCF Technical Oversight Committee (TOC) recognizes HAMi's mature technical and security practices, active community, real production adoption, and open ecosystem integration.
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.
