AI Infra Interviews
Work through the numbers behind GPU systems, from a single model to a training cluster. Learn to explain your decisions and handle the follow-up.
130 answers to read now · 260 with a free account · no card
A question to start with
Change the model or context. Watch the memory budget change.
KV/token = 2 × 80 layers × 8 KV heads × 128 × 2 B
Memory estimate only. Actual serving capacity depends on parallel layout, runtime overhead and latency targets.
GPU LAB / HANDS-ON GPU COMMAND EMULATOR
Run inference and training workloads. Watch memory fill, follow live GPU graphs, then investigate a broken link or a service that refuses to start. Practice NVIDIA commands in your browser, with guided missions from your first terminal session to cluster recovery.
3 free missions · 21 Premium missions · no hardware to rent
Change the workload. Read the machine.
Run a workload on GPU 0. Follow its activity and memory, pause the clock, then switch workloads to compare.
No account or GPU required. Starts only when you press Run.
Find your way around Linux, identify GPUs and separate activity from allocation.
02 / PREMIUMRead configuration, budget memory and verify the address your client uses.
03 / PREMIUMTrace device placement, inspect the fabric and recover a failed endpoint.
04 / PREMIUMCorrelate NVLink, InfiniBand and ECC evidence. Separate current device state from historical errors.
05 / PREMIUMRepair configuration, trade context for concurrency and verify placement without disrupting neighboring workloads.
06 / PREMIUMDiagnose several independent blockers, verify the complete local path and leave a useful handoff.
Browser-based GPU command emulation. CUDA execution and hardware performance testing require real GPUs.
Interactive explainer · runs in your browser
Follow one prompt through tokens, prefill, the KV cache, decode and sampling. Every number on the page comes from a real model running the request: the time each step takes, the bytes it reads and the memory it keeps.
Free to explore; the production-scale stage is Premium. No account or GPU needed.
Learn what the machine does, what the model stores, and why an answer can be slow. Then try a memory budget and choose your first lesson.
Start learning the basics →Five steps · free to read · no account or GPU needed
Build it step by step
Train a tiny model, compare fine-tuned adapters, or configure and tune a private model service. Follow the commands and inspect what each step produces.
Your first training run
Free to readBuild a 470,528-parameter causal language model on a CPU. Train it, inspect validation loss, save a checkpoint and resume the same run.
CPU · Python 3.12 · no GPU required
A trained byte-level model, a resumable checkpoint and sampled text.
CPU workflow executed
CPU workflow executed across five seeds
Planner and client CPU-executed; model run pending
GPU qualification and timing executed
13 CPU tests executed
See how we teach
What happens between your prompt and the next token? Follow the loop in this diagram from our inference platform walkthrough.
Free to read · no account needed
Open the answer. Check the reasoning.
GPU & Accelerator Architecture
Hardware, Cabling & Cluster Build-Out
CUDA, Triton & Kernel Engineering
Distributed Training & Parallelism
A free account gives you 260 answers, bookmarks and saved progress.
Prepare for the work you want to do
Live CUDA or Triton, a kernel take-home, the roofline, FlashAttention internals.
NVIDIA CUDA teams, Fireworks, Together, lab performance teams02Parallelism strategy, collectives and fabrics, MFU, checkpointing, what breaks at 10k GPUs.
Frontier labs, xAI, Crusoe, hyperscaler training teams03The KV cache, batching, speculation, disaggregation, and the design round with TTFT on the board.
OpenAI, Anthropic, Baseten, Together, Fireworks, Red Hat04Kubernetes GPU scheduling, MIG, Slurm, DCGM and XID codes, the on-call reality.
CoreWeave, Modal, Nebius, Lambda, ML platform teamsBuild the foundations
Follow the lessons in order, then put the ideas to work.
Explore all 7 courses →15 lessons · 3 free to read
15 lessons · 5 free to read
26 lessons · 2 free to read
Go deeper when you’re ready
Work through the full bank, study the underlying concepts, and follow a course from the foundations into system design.
One payment. 6 months of access. No auto-renewal.
Full access
Start with a free account and decide after you’ve studied.
Focus your preparation
Explore reported interview formats and relevant topics. Each guide distinguishes documented details from limited public evidence. Loops vary by team; confirm yours with the recruiter.
Independent preparation. No affiliation with or endorsement by the companies named.
AI INFRA CAREERS · FREE
Explore GPU, training, inference and platform openings at companies we track. Filter by specialty and location, then prepare for that company.
Guides & handbooks
Our nine latest guides, from a first model request to the systems behind a production fleet. Explore the contents and sample pages before choosing your next read. Complete Premium guides require paid or approved access.
110 terms, worked cases and an illustrated PDF reference.
80 pages · 110 terms · Premium PDFExplore the guide
Hardware, networking, training and inference from the beginning.
56 pages · 25 illustrations · Premium PDF + laptop labExplore the guide
Interactive agents, reinforcement learning and evaluation.
41 pages · 22 chapters · Premium PDF + tested companionExplore the guide
vLLM and SGLang operations, metrics and runbooks.
102 pages · 29 chapters · Premium PDF + tested companionExplore the guide
Cabling, NVLink, InfiniBand, RoCE and security.
63 pages · 20 diagrams · Premium PDF + companionExplore the guide
GPU passthrough, B200 and enterprise firewalls.
34 pages · 6 demo screenshots · Premium PDFExplore the guide
From the first estimate to a design you can defend
53 pages · 19 diagrams · Python companionExplore the guide
DeepSeek, Qwen, Kimi, GLM and gpt-oss in production
37 pages · 13 diagrams · Python companionExplore the guide
Rollouts, weight sync and agentic RL at scale
35 pages · 12 diagrams · Python companionExplore the guidePAID REMOTE INTERNSHIP
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