NVIDIA / ACCELERATED AI SYSTEMS
Understand the machine.
Build the capability.
Start with what a GPU does. Then connect CUDA, Nemotron, NeMo and model serving to a real engineering decision: what to run, where to run it, and how to pay for it.
Start with the visual introduction →- 01CPU: coordinate
- 02GPU: parallel math
- 03Memory: feed the work
ONE CONNECTED LEARNING PATH
From first principles to a deployment decision.
Preview every concept freely. Use the Academy for guided explanations, knowledge checks and saved practice with your existing training access.
- NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Application + tools
- 02Nemotron · NIM · NeMo
- 03GPU + CUDA
01 / An ecosystem of hardware, models and softwareWhere does NVIDIA fit in an AI system?
An FDE must explain which component solves the client's problem, who operates it and what evidence supports using it. That is more useful than presenting a wall of product logos.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01CPU: coordinate
- 02GPU: parallel math
- 03Memory: feed the work
02 / A processor for parallel numerical workWhat is a GPU, and why does AI use it?
You need to tell whether the workload benefits from acceleration before renting expensive capacity or promising a response time.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Python / framework
- 02CUDA libraries
- 03GPU kernels
03 / A GPU computing platform and programming modelWhat is CUDA?
A deployment engineer must diagnose whether the model is using the intended device and record a reproducible software environment.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Model weights
- 02Active request state
- 03Runtime + headroom
04 / Capacity, bandwidth and numerical precisionWhy does GPU memory matter so much?
This is the bridge from a model choice to a credible capacity plan, admission limit and procurement request.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01GPU + memory
- 02NVLink domain
- 03Cluster network
05 / GPU generations, interconnects and complete systemsHow do modern GPU systems work together?
An FDE must translate 'latest GPU' into a workload, a compatible complete system and a dated availability check.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Exact model + revision
- 02Your evaluation set
- 03Accept / revise / reject
06 / NVIDIA's family of open AI modelsWhat is Nemotron?
A client needs a model selected on task quality, data constraints, cost and operability, not merely brand familiarity.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Curate / prepare
- 02Customize / configure
- 03Evaluate / compare
07 / A modular development and improvement software suiteWhat is NeMo, and when would I use it?
An FDE should diagnose whether poor results come from missing knowledge, unclear instructions, model behavior or application defects before proposing customization.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Authenticated application
- 02NIM / serving runtime
- 03Compatible GPU system
08 / Model serving, optimized execution and distributed orchestrationHow do NIM and the inference tools fit together?
The FDE connects model execution to an authenticated service, capacity limits, monitoring, an update policy and a rollback plan.
Explore the visual 101 → - NVIDIA / SYSTEMS IN CONTEXTCONCEPT VIEW
- 01Managed endpoint
- 02Rent GPU compute
- 03Own a complete system
09 / Workload sizing, procurement and total costHow do you buy or rent GPU capacity?
An FDE should produce a decision that procurement and operations can act on, with a pilot, measured acceptance conditions and a dated quote.
Explore the visual 101 →
YOUR PRACTICE / NO GPU REQUIRED
Make the decision before buying the hardware.
Run a local memory-budget experiment, compare hypothetical rent-versus-own costs, and assemble a procurement brief with the evidence still needed. The lab uses synthetic inputs and does not claim hardware benchmark results.
Open the lab and decision workbook →Continue in the Academy
Worked examples, review criteria and a transfer challenge connect each lesson to the same private maintenance-assistant case.
Open the guided NVIDIA course →