THE DEV BENCH

NVIDIA AI Infrastructure & Operations

One workspace for a single goal: the skills a datacenter GPU / AI-infrastructure & operations role screens for. Certs, decks, hands-on labs, a skill map, and curated reading — all in one place.

The honest read: this is an operations skill set — reading GPU tool output, editing config, and fixing broken clusters. Most of it can be practiced without owning datacenter hardware; only a few things (MIG, multi-GPU NCCL) need a rented GPU. The plan below reflects that.

Deep work · NVIDIA AI Infratotal
or log

Certification roadmap

sequence
NCA-AIIONVIDIA-Certified Associate: AI Infrastructure & OperationsDo now

Associate · $125 · 50 MCQ, 60 min, no labs

Conceptual — plays to your strength (understand the infra without having racked it). The 50-card cert deck below is domain-weighted to this exam.

CKACertified Kubernetes AdministratorIn parallel

Professional · hands-on, live cluster

GPU orchestration runs on Kubernetes. The K8s Learn + Break-fix labs below ARE the study plan.

NCP-AIONVIDIA-Certified Professional: AI OperationsThe goal

Professional · hands-on labs

The only software/ops-focused NVIDIA Professional (Slurm, Kubernetes, Base Command Manager, workload mgmt) — not rack-and-stack. Sequence after CKA + NCA-AIIO + the homelab GPU labs exist.

Flashcard decks — the recall layer

ready now
NVIDIA NCA-AIIO — AI Infrastructure & Operations (Associate)50 exam-style cards, weighted to the official domains (Essential AI 38% · Infrastructure 40% · Operations 22%), each with a plain-language jargon decoder. Start here.

Hands-on labs — the real practice

live

These build the ops muscle GPU-infra interviews test (the live break-fix format is exactly this). All run in the browser.

GPU labs — on the build list

planned

The GPU-specific interactive labs, in build order. Non-GPU ones come first (they need no hardware); the rest run on stubs, then a real GPU (your 5060 for single-GPU, rented for datacenter-class).

CardLabWhat you'll doBackend
TDB-64GPU triage & break-fixXID / ECC / throttle / driver-mismatch. Stub nvidia-smi first, then real on the 5060.stub → 5060
TDB-64NVIDIA Container ToolkitMake a container actually see the GPU; fix the runtime hook.stub → 5060
TDB-66DCGM → Prometheus → GrafanaStand up GPU observability and alert on temp/throttle (synthetic metrics).no GPU needed
TDB-67NCCL / MIG / HPLMulti-GPU all-reduce, MIG partitioning, Linpack — needs real datacenter GPUs.RunPod (rented)
TDB-68AI networking (InfiniBand / RDMA)Topology and fabric reasoning — mostly config exercises + decks.decks + config

Skill map — where to practice each thing

the workspace
SkillPractice withStatus
GPU fundamentals & the CUDA stackNCA-AIIO deckready
Containers for GPUsDocker lab · Container Toolkit labpartly ready
Orchestration & schedulingKubernetes lab · Slurm labpartly ready
GPU observability (DCGM)DCGM → Grafana labplanned
GPU troubleshooting (XID/throttle)GPU triage labplanned
Multi-GPU comms (NCCL)NCCL lab (real GPU)planned
MIG partitioningMIG lab (real GPU)planned
Networking (InfiniBand/RDMA)Networking + OSI decksready
MLOps lifecycleAI-300 MLOps deckready

A study routine

  1. 1.Drill the NCA-AIIO deck daily — recall out loud before revealing, mark the hard ones. ~15 min.
  2. 2.One hands-on lab session a week (K8s, Docker, or Linux) to build real ops muscle.
  3. 3.Read or watch one resource from the list below each week, then add its key idea as a card.
  4. 4.Track everything on My Bench so nothing goes stale.

Interview readiness

when a screen is coming

When you land a screen for a role like this, the study plan below turns everything above into a focused prep sprint — how the conversational technical interview works, how to position yourself, the talking points to have ready, and a 21-question mock-interview drill deck to rehearse out loud.

AI Infra & Operations — Interview ReadinessPositioning, talking points, a mock-question drill deck, and a short prep sprint.

Curated resources — free-first

verified

Every link below was checked live. The strongest picks are here; prices/versions shift, so treat costs as “as seen.”

Start here

Official NVIDIA — courses, certs & docs

Videos & talks

Books

Free hands-on repos

Audiobooks: they basically don't exist for GPU-infra.

The topics are code/diagram/CLI-heavy and aren't published narrated — ignore any “AI-narrated” listings claiming to be these books. Closest audio substitute:

Full verified list (docs, whitepapers, more repos): docs/NVIDIA_AI_INFRA_RESOURCES.md in the repo.

Start your reps