$ vishctl get notes -l topic=kubernetes
A simple, diagram-first comparison of SGLang and vLLM: how RadixAttention works, how jump-forward decoding speeds up JSON outputs, how their schedulers compare, and which engine fits your workload.
A hands-on lab for learning GPU scheduling and LLM routing without a GPU: kind, fake-gpu-operator, llm-d-inference-sim, and the llm-d router, with real screenshots and benchmark numbers.
A practical, diagram-first guide to llm-d: why round-robin wastes GPUs, how cache-aware routing works, which well-lit path to start with, and the commands to deploy it.
A hands-on Temporal demo in Python: build a small order workflow, deploy it on Kubernetes, then delete the worker pods mid-execution and watch durable execution resume it.
A simple, layered guide to GPUs on Kubernetes: GPU Operator, NUMA and PCIe, RDMA, SR-IOV and Multus, NVLink and NCCL, with a troubleshooting question for every layer.
Karmada's CNCF graduation, its multi-cluster architecture, and practical YAML for placement, replica splitting, overrides, and failover.
How PagedAttention, continuous batching, hardware portability, and an open ecosystem made vLLM the default engine for high-throughput LLM serving.
A practical guide to running vLLM on Kubernetes in WSL2 with Minikube, configuring NVIDIA GPU passthrough, and serving an OpenAI-compatible API.
Kubernetes 1.37 makes KYAML stable: how flow style eliminates silent type coercion, removes load-bearing indentation, and how to try it.
My notes on Kubernetes v1.37: native HPA scale-to-zero, tracking unused PVCs, ClusterTrustBundles, and etcd streaming.