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 policies, and look beyond the default scheduler at KAI Scheduler, Volcano, and DRA.
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.
GPU sharing is moving from soft allocation to governance—scheduling decisions and runtime isolation can finally be reconciled.
Olares and HAMi: Desktop AI Workstation Inflection
HAMi moves from cluster to desktop with Olares.
Why GPU Is the Foundation of AI
A GPU explainer for Kubernetes veterans new to AI. Maps token, model, training, inference, Transformer, Tensor Core, HBM, and KV cache to concepts you already know.
From GPU utilization to productive GPU-hours.
From GPU to Token: The 8-Layer Observability Stack for AI Infrastructure
From GPU hardware, Kubernetes scheduling, inference engines to token cost — understanding the 8-layer observability architecture for modern AI infrastructure.
Token Is More Than a Billing Unit, It's Becoming the Resource Unit of the AI Era
The Linux Foundation’s Tokenomics Foundation signals a shift: tokens are becoming a core resource in the AI era, much like CPUs in the cloud era.
AI Infra Industry Trends: From Compute Bottlenecks to Ecosystem Evolution
A practitioner’s perspective on AI infrastructure trends: evolving bottlenecks, roles of CPU/GPU/scheduling, ecosystem shifts, and compute demand across training, inference, and Agent workloads.