Module 1 — Introduction to AI Compute
A simple, beginner-friendly introduction to the hardware behind modern AI.
You will learn:
- Why AI models require specialized compute
- The difference between CPUs and GPUs in practical terms
- What FLOPS, parallelism, and tensor operations mean
- Where compute bottlenecks appear in AI workloads
- How training vs inference loads differ
Outcome:
You understand why GPUs power AI and what makes them so effective.
Module 2 — GPU Fundamentals (Explained Simply)
A clear breakdown of how a GPU works inside and why it’s ideal for ML.
Topics covered:
- GPU architecture basics
- Tensor cores, CUDA cores, memory hierarchy
- Types of memory (HBM, VRAM) and why they matter
- Consumer GPUs vs Data Center GPUs: key differences
- What makes training-oriented GPUs different from inference GPUs
Outcome:
You can confidently explain how GPUs operate and why AI depends on them.
Module 3 — The NVIDIA AI Ecosystem
A full overview of the tools, hardware, and platforms NVIDIA provides for AI.
You will explore:
- What CUDA is and why it changed everything
- cuDNN, TensorRT, NCCL — what these libraries do
- Overview of modern GPU lineup:
- A100
- H100
- GH200 Grace Hopper Superchip
- Why NVIDIA dominates the AI hardware market
- How developers interact with CUDA-based systems
Outcome:
You understand the key NVIDIA technologies powering today’s AI models.
Module 4 — Inside an AI-Ready Data Center
A beginner-safe introduction to the physical and logical design of data centers built for AI workloads.
Topics include:
- What makes a data center “AI-capable”
- Power delivery and why AI hardware consumes so much electricity
- Cooling systems: air, liquid, immersion
- Networking fundamentals:
- InfiniBand
- NVLink
- High-throughput topologies
- How large clusters are physically organized
Outcome:
You get a clear picture of how AI data centers are constructed and what keeps them running.
Module 5 — How AI Clusters Are Built
Step-by-step breakdown of building and running distributed compute systems.
You will learn:
- Single-node vs multi-node setups
- Distributed training fundamentals (simple, digestible explanation)
- GPU interconnects and why bandwidth matters
- Basics of orchestration:
- Kubernetes
- SLURM
- Ray
- How hyperscalers (AWS, GCP, Azure) organize their clusters
Outcome:
You understand how multiple GPUs and machines work together to train large AI models.
Module 6 — Cloud GPUs & Practical Usage
How to use GPUs in the cloud without getting lost or overpaying.
Topics covered:
- Cloud GPU options: AWS, GCP, Azure, Lambda, CoreWeave
- On-demand, reserved, and spot pricing
- How to choose a GPU for:
- training
- fine-tuning
- inference
- Cost optimization strategies
- How to avoid beginner mistakes (like overprovisioning or wrong instance selection)
Outcome:
You know how to navigate cloud GPU offerings with confidence.
Module 7 — Practical AI Infrastructure Planning
A hands-on module where theoretical knowledge converts into real skills.
You will do:
- Estimate compute requirements for an AI model
- Compare cloud vs on-premise options
- Build a simple “infrastructure plan” for a real use case
- Understand monitoring basics
- Learn how small teams can build efficient setups on a budget
Outcome:
You can create a basic but realistic AI compute strategy for your own projects.
🏁 Final Project
Design a simple AI infrastructure plan for a specific scenario:
- choose a GPU setup
- evaluate training costs
- estimate resource needs
- outline a cluster or cloud approach
- justify your decisions with real metrics
This is a short but practical assignment that solidifies everything you’ve learned.
AI Infrastructure for Beginners: From GPUs to Cloud Clusters
This course costs $249 and lasts 4 weeks. To enroll, please fill out the registration form below — it only takes a moment and is required to activate your course access.
To register for the course, click here.
