The HAMi community is proud to announce the official release of HAMi v2.11.0.
Highlights of this release:
- Remote GPU: run CUDA and vLLM Pods on GPU-less, driver-less client nodes, using GPUs served over the network by Lupine GPU servers.
- AMD Instinct GPU sharing: AMD GPUs (ROCm) are now first-class in HAMi, with memory limits by size or percentage, compute limits, NUMA- and XGMI-aware placement, scheduling policies and queue priorities, all enforced at the container level.
- Ascend ENPU mode: policy-based isolation (
fixed-share, elastic, best-effort) as a third partitioning mode alongside templates and hami-core, plus a major hami-vnpu-core performance overhaul.
- NUMA refit: kubelet's Topology Manager and HAMi's scheduler now agree, so CPU affinity and GPU placement land on the same NUMA node.
- Flexible MIG grows up: CDI injection for dynamic MIG devices, placement scoring with backtracking, and per-Pod profile preferences.
- A harder scheduler: security-audit-driven hardening of the scheduler extender and webhook, plus Filter-path performance gains.
- Platform and chart: OpenShift and SELinux support, a restructured
devices.VENDOR chart, and node capacity resource reporting.
- Website overhaul: reorganized information architecture, a redrawn Concepts section,
llms.txt, and restructured installation and upgrade guides.
In total, v2.11.0 merges more than 220 pull requests from over 50 contributors, including 29 first-time contributors.
This article walks through the major updates in v2.11.0.
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.
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.
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.
