N · cluster
cluster
Scheduler, GPU partitioning, autoscaling, node lifecycle — the layer that decides where a workload lands.
Mechanism reference for Kubernetes at the helm of AI
One article, one mechanism, with the real APIs and the real failure modes. Forty charts across four surfaces.
E · serve
serve
Inference servers, KV-cache, batching, streaming, model routing — the layer that answers requests.
S · train
train
Distributed training, NCCL, checkpointing, fault tolerance — the layer that turns GPU-hours into weights.
W · operate
operate
Observability, security, GitOps, cost — the layer that keeps the other three honest.
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Model weights belong in object storage, not container images
Checkpoint storage patterns for distributed training
How priority and preemption interact with GPU pods
Why the NVIDIA gpu-operator upgrade window is non-trivial
What Velero backs up on an AI cluster