kubernetes

MIG carves an H100 into up to seven hardware slices with their own SMs, memory and bandwidth; MPS lets processes share one SM pool through a single context. One gives fault and performance isolation at a fixed slice size; the other gives flexibility and a shared failure domain. The numbers that decide it.

The four layers between `nvidia.com/gpu: 4` and a container that can run CUDA, the one failure each layer produces, the stranded-GPU arithmetic the integer model causes, and why Dynamic Resource Allocation replaces the count with a claim.

Hardware partitions, shared SMs, and context switching are three different promises about isolation and waste. The slice arithmetic for an H100, what each mode gives up, and the one rule about tenants that decides most of it.

Slurm gives you gang scheduling, topology and MPI launch for free and nothing for services; Kubernetes gives you services, elasticity and the ecosystem and makes you bolt on the batch semantics. The hybrid that most large clusters land on, with the numbers that push them there.

A distributed training job is 64 pods that start together or not at all. The deadlock two partially placed jobs produce, counted out on a 64-GPU cluster, and how Kueue and Volcano make the job the unit of admission.

Fractions are either hardware slices with fixed shapes or soft shares with no isolation, and the scheduler has to know which. The representation, the packing score, the fragmentation arithmetic that spread placement produces, and the defragmentation step that fixes it.

A node with eight healthy H100s that Kubernetes thinks has none. The layered check from kernel driver to container toolkit to device plugin to taints and allocatable, in the order that isolates the fault fastest, with the log lines each layer prints when it is the one that broke.

A GPU node needs a driver, a container toolkit, a device plugin, a metrics exporter and a validator, in that order, and each needs to know which nodes it belongs on. The operator's component chain, the labels NFD writes to trigger it, and the bring-up arithmetic that rules out doing it by hand.

Ray gives you actors, tasks and placement groups inside a cluster of pods that Kubernetes thinks are just pods. Two schedulers, two views of the same GPUs: the idle-node arithmetic, the placement group that cannot be gang-scheduled by the layer below, and the rules that stop the two from wasting each other's capacity.

Device plugins count GPUs and know nothing else; Dynamic Resource Allocation lets a pod ask for devices by attribute and share them by claim. What the two models can and cannot express, what DRA changes for MIG, NVLink domains and multi-node gangs, and a migration stance for a fleet that runs both.

A plain Deployment reports ready before it can serve and scales on a signal that never moves. What an inference operator adds: weight delivery, readiness tied to warm-up, scaling on queue depth with cold starts accounted for, a prefix-aware router, and rollouts that keep streams alive. KServe and llm-d as of 2026.

A pod's default network is an overlay that no RDMA NIC will speak. The three ways to give a pod the fabric (host networking, SR-IOV virtual functions via a NIC device plugin, or a macvlan secondary interface), what each costs in isolation and speed, how NCCL finds the right NIC, and the failure that halves bandwidth.