A GPU Workshop and Cloud Native's Developer Experience Gap
Notes from ContainerDays Singapore 2026.
HAMi
From Zero to Shared GPUs: Hands-on GPU Scheduling in Kubernetes with HAMi
A 45-minute hands-on workshop at ContainerDays 2026 Singapore. Install HAMi on a kind cluster with two fake A100s, share one GPU across Pods, steer binpack and spread scheduler …
Why We Still Need GPU Partitioning
Bigger models make GPU partitioning matter more.
KubeCon China 2026 Recap
Returning to Shanghai after eight years, I see cloud native becoming the infrastructure foundation for AI.
GPU Scheduling Landscape and mutex Semantics
Why GPU scheduling matters: how the default scheduler misses node, card, and topology placement, how HAMi v2.10 fills the gaps, and what mutex really means on kind.
HAMi-core and KAI Scheduler
GPU sharing is moving from soft allocation to governance—scheduling decisions and runtime isolation can finally be reconciled.
HAMi Community Evolution: When AI Writes Code, What Makes an Open Source Community Valuable?
A long-term participant’s observation on HAMi’s growth (2021 open source, 2024 CNCF Sandbox, 2026 CNCF Incubating) and what makes an open source community valuable in …
After HAMi's CNCF Incubating: From a Community of Code to a Network of Consensus
Code is cheap; consensus is the new scarce good.
Olares and HAMi: Desktop AI Workstation Inflection
HAMi moves from cluster to desktop with Olares.
GPU Utilization Is Breaking
From GPU utilization to productive GPU-hours.