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.
Certification roadmap
sequenceAssociate · $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.
Professional · hands-on, live cluster
GPU orchestration runs on Kubernetes. The K8s Learn + Break-fix labs below ARE the study plan.
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 nowHands-on labs — the real practice
liveThese 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
plannedThe 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).
| Card | Lab | What you'll do | Backend |
|---|---|---|---|
| TDB-64 | GPU triage & break-fix | XID / ECC / throttle / driver-mismatch. Stub nvidia-smi first, then real on the 5060. | stub → 5060 |
| TDB-64 | NVIDIA Container Toolkit | Make a container actually see the GPU; fix the runtime hook. | stub → 5060 |
| TDB-66 | DCGM → Prometheus → Grafana | Stand up GPU observability and alert on temp/throttle (synthetic metrics). | no GPU needed |
| TDB-67 | NCCL / MIG / HPL | Multi-GPU all-reduce, MIG partitioning, Linpack — needs real datacenter GPUs. | RunPod (rented) |
| TDB-68 | AI networking (InfiniBand / RDMA) | Topology and fabric reasoning — mostly config exercises + decks. | decks + config |
Skill map — where to practice each thing
the workspace| Skill | Practice with | Status |
|---|---|---|
| GPU fundamentals & the CUDA stack | NCA-AIIO deck | ready |
| Containers for GPUs | Docker lab · Container Toolkit lab | partly ready |
| Orchestration & scheduling | Kubernetes lab · Slurm lab | partly ready |
| GPU observability (DCGM) | DCGM → Grafana lab | planned |
| GPU troubleshooting (XID/throttle) | GPU triage lab | planned |
| Multi-GPU comms (NCCL) | NCCL lab (real GPU) | planned |
| MIG partitioning | MIG lab (real GPU) | planned |
| Networking (InfiniBand/RDMA) | Networking + OSI decks | ready |
| MLOps lifecycle | AI-300 MLOps deck | ready |
A study routine
- 1.Drill the NCA-AIIO deck daily — recall out loud before revealing, mark the hard ones. ~15 min.
- 2.One hands-on lab session a week (K8s, Docker, or Linux) to build real ops muscle.
- 3.Read or watch one resource from the list below each week, then add its key idea as a card.
- 4.Track everything on My Bench so nothing goes stale.
Interview readiness
when a screen is comingWhen 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
verifiedEvery 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.