HAMi becomes a CNCF incubating project

The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project. About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and... The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project.  About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and underused because whole devices get allocated to jobs that only need a fraction of one, teams compete for scarce accelerators, and every hardware vendor exposes a different operational model. Scheduling these heterogeneous accelerators well requires device-level context that goes beyond what’s needed for general-purpose compute. HAMi solves this by providing an open source, cloud native GPU virtualization middleware for Kubernetes . With HAMi, platform teams can slice a physical GPU (or NPU, DCU, MLU, or other accelerator) into units by memory, core, or device count; enforce hard runtime isolation between the workloads sharing it; and schedule pods using binpack, spread, and topology-aware policies — all without touching application code or existing Kubernetes resource manifests. HAMi has a multi-vendor design with a single, consistent interface, which sets it apart from device-plugin tooling built around one vendor’s ecosystem. HAMi’s key milestones and ecosystem development Since joining the CNCF Sandbox on August 21, 2024, HAMi has seen significant growth in adoption, contribution, development, and ecosystem reach. The project counts more than 550 contributing organizations, and five independent CNCF case studies have now been published, documenting production use spanning education, cloud platforms, and enterprise technology — including DaoCloud’s deployment of HAMi across more than 10,000 GPUs in over 10 data centers in mainland China and Hong Kong and China Merchants Bank’s use of the proje

The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project. About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and... The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project.  About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and underused because whole devices get allocated to jobs that only need a fraction of one, teams compete for scarce accelerators, and every hardware vendor exposes a different operational model. Scheduling these heterogeneous accelerators well requires device-level context that goes beyond what’s needed for general-purpose compute. HAMi solves this by providing an open source, cloud native GPU virtualization middleware for Kubernetes . With HAMi, platform teams can slice a physical GPU (or NPU, DCU, MLU, or other accelerator) into units by memory, core, or device count; enforce hard runtime isolation between the workloads sharing it; and schedule pods using binpack, spread, and topology-aware policies — all without touching application code or existing Kubernetes resource manifests. HAMi has a multi-vendor design with a single, consistent interface, which sets it apart from device-plugin tooling built around one vendor’s ecosystem. HAMi’s key milestones and ecosystem development Since joining the CNCF Sandbox on August 21, 2024, HAMi has seen significant growth in adoption, contribution, development, and ecosystem reach. The project counts more than 550 contributing organizations, and five independent CNCF case studies have now been published, documenting production use spanning education, cloud platforms, and enterprise technology — including DaoCloud’s deployment of HAMi across more than 10,000 GPUs in over 10 data centers in mainland China and Hong Kong and China Merchants Bank’s use of the project to manage diverse accelerator resources at scale. The project shows strong community interest, with roughly 3,500 GitHub stars and more than 550 forks in the main repository. HAMi’s contributor base has seen explosive growth, totaling 2,687 contributors across GitHub with an impressive 43% increase YoY. Maintainers span multiple companies, including dynamia.ai and NVIDIA, alongside independent developers, reflecting the vendor-neutral governance CNCF incubation requires. HAMi has shipped 16 releases, with the current stable version at v2.9.0 . HAMi continues to deepen ties in the CNCF ecosystem, integrating with Volcano for batch-oriented AI scheduling and with Koordinator for GPU-sharing workflows, while remaining compatible with the default Kubernetes scheduler path. Maintainers have discussed further integration with projects like Kueue to build out a more complete cloud native AI infrastructure stack. “When I attended KubeCon Paris back in 2024, HAMi had just been open-sourced. I remember hoping that one day it would become as open and vibrant as the other projects on display at the event. Two years have flown by, and HAMi  has grown into a recognized CNCF incubating project with an international community. None of this would have been possible without the dedication of every contributor, every user, and the invaluable support of CNCF. Looking ahead, HAMi is more than just a middleware — it aspires to become a hub of best practices for every kind of heterogeneous device. We’d love for you to stay tuned and be part of what comes next.” — Mengxuan Li , Maintainer, HAMi “Seeing HAMi reach Incubation is a proud moment for all of us. HAMi now supports dozens of Heterogeneous GPU and has grown into a global community with hundreds of contributors and hundreds of end users. What makes this milestone meaningful is not just the technology, but the real ecosystem momentum behind it. We’re excited to keep building with the broader community.” — Xiao Zhang , Maintainer, HAMi The CNCF Technical Oversight Committee (TOC) provides technical leadership to the cloud native community. It defines and maintains the foundation’s technical vision, approves new projects, and stewards them across maturity levels. The TOC also aligns projects within the overall ecosystem, sets cross-cutting standards and best practices, and works with end users to ensure long-term sustainability. As part of its charter, the TOC evaluates and supports projects as they meet the requirements for incubation and continue progressing toward graduation. “HAMi solves a real problem: scheduling and sharing accelerator resources on Kubernetes in a way that works across vendors. Since entering Sandbox, the project has grown a multi-vendor contributor base and advanced technically on the infrastructure challenge that matters most as AI workloads scale on Kubernetes. The TOC is pleased to see HAMi reach Incubation and will continue supporting the project as it matures alongside the broader cloud native AI ecosystem.” — Karena Angell , CNCF TOC Sponsor HAMi’s main components HAMi is composed of several components: Mutating Webhook: Intercepts pod submissions in the Kubernetes API server and rewrites scheduler fields and resource requests for workloads requesting virtualized devices. Scheduler Extender: Filters, scores, and binds pods to nodes and devices using binpack, spread, and topology-aware placement policies. Device Plugins: Vendor-specific plugins that register accelerators with Kubernetes and allocate fractional device resources to containers. HAMi-Core: The in-container virtualization layer that enforces hard runtime limits on GPU memory and compute, intercepting the native CUDA driver for NVIDIA devices. HAMi-WebUI: A visual interface for cluster and device management, giving operators visibility into allocation and utilization across the fleet. Observability Layer: A Prometheus -compatible metrics endpoint and Grafana dashboard examples for

The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project. About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and... The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project.  About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and underused because whole devices get allocated to jobs that only need a fraction of one, teams compete for scarce accelerators, and every hardware vendor exposes a different operational model. Scheduling these heterogeneous accelerators well requires device-level context that goes beyond what’s needed for general-purpose compute. HAMi solves this by providing an open source, cloud native GPU virtualization middleware for Kubernetes . With HAMi, platform teams can slice a physical GPU (or NPU, DCU, MLU, or other accelerator) into units by memory, core, or device count; enforce hard runtime isolation between the workloads sharing it; and schedule pods using binpack, spread, and topology-aware policies — all without touching application code or existing Kubernetes resource manifests. HAMi has a multi-vendor design with a single, consistent interface, which sets it apart from device-plugin tooling built around one vendor’s ecosystem. HAMi’s key milestones and ecosystem development Since joining the CNCF Sandbox on August 21, 2024, HAMi has seen significant growth in adoption, contribution, development, and ecosystem reach. The project counts more than 550 contributing organizations, and five independent CNCF case studies have now been published, documenting production use spanning education, cloud platforms, and enterprise technology — including DaoCloud’s deployment of HAMi across more than 10,000 GPUs in over 10 data centers in mainland China and Hong Kong and China Merchants Bank’s use of the project to manage diverse accelerator resources at scale. The project shows strong community interest, with roughly 3,500 GitHub stars and more than 550 forks in the main repository. HAMi’s contributor base has seen explosive growth, totaling 2,687 contributors across GitHub with an impressive 43% increase YoY. Maintainers span multiple companies, including dynamia.ai and NVIDIA, alongside independent developers, reflecting the vendor-neutral governance CNCF incubation requires. HAMi has shipped 16 releases, with the current stable version at v2.9.0 . HAMi continues to deepen ties in the CNCF ecosystem, integrating with Volcano for batch-oriented AI scheduling and with Koordinator for GPU-sharing workflows, while remaining compatible with the default Kubernetes scheduler path. Maintainers have discussed further integration with projects like Kueue to build out a more complete cloud native AI infrastructure stack. “When I attended KubeCon Paris back in 2024, HAMi had just been open-sourced. I remember hoping that one day it would become as open and vibrant as the other projects on display at the event. Two years have flown by, and HAMi  has grown into a recognized CNCF incubating project with an international community. None of this would have been possible without the dedication of every contributor, every user, and the invaluable support of CNCF. Looking ahead, HAMi is more than just a middleware — it aspires to become a hub of best practices for every kind of heterogeneous device. We’d love for you to stay tuned and be part of what comes next.” — Mengxuan Li , Maintainer, HAMi “Seeing HAMi reach Incubation is a proud moment for all of us. HAMi now supports dozens of Heterogeneous GPU and has grown into a global community with hundreds of contributors and hundreds of end users. What makes this milestone meaningful is not just the technology, but the real ecosystem momentum behind it. We’re excited to keep building with the broader community.” — Xiao Zhang , Maintainer, HAMi The CNCF Technical Oversight Committee (TOC) provides technical leadership to the cloud native community. It defines and maintains the foundation’s technical vision, approves new projects, and stewards them across maturity levels. The TOC also aligns projects within the overall ecosystem, sets cross-cutting standards and best practices, and works with end users to ensure long-term sustainability. As part of its charter, the TOC evaluates and supports projects as they meet the requirements for incubation and continue progressing toward graduation. “HAMi solves a real problem: scheduling and sharing accelerator resources on Kubernetes in a way that works across vendors. Since entering Sandbox, the project has grown a multi-vendor contributor base and advanced technically on the infrastructure challenge that matters most as AI workloads scale on Kubernetes. The TOC is pleased to see HAMi reach Incubation and will continue supporting the project as it matures alongside the broader cloud native AI ecosystem.” — Karena Angell , CNCF TOC Sponsor HAMi’s main components HAMi is composed of several components: Mutating Webhook: Intercepts pod submissions in the Kubernetes API server and rewrites scheduler fields and resource requests for workloads requesting virtualized devices. Scheduler Extender: Filters, scores, and binds pods to nodes and devices using binpack, spread, and topology-aware placement policies. Device Plugins: Vendor-specific plugins that register accelerators with Kubernetes and allocate fractional device resources to containers. HAMi-Core: The in-container virtualization layer that enforces hard runtime limits on GPU memory and compute, intercepting the native CUDA driver for NVIDIA devices. HAMi-WebUI: A visual interface for cluster and device management, giving operators visibility into allocation and utilization across the fleet. Observability Layer: A Prometheus -compatible metrics endpoint and Grafana dashboard examples for