Why We Still Need GPU Partitioning
Bigger models make GPU partitioning matter more.
AI Engineering
LLM, AI Native Infra and Agentic 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.
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.
GPU Utilization Is Breaking
From GPU utilization to productive GPU-hours.
Agentic AI Infrastructure Reliability
A practical AI Infra review of Agentic AI reliability, covering a five-dimension framework, fault tolerance, recovery, observability, and hybrid architecture design.
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.
My Personal AI Stack
How I built a personal AI infrastructure using ChatGPT, OpenClaw, Obsidian, GitHub, Lark, GLM-5.1, and a Mac mini M4.
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, …