The global AI boom has turned GPUs into one of the most strategically important resources in technology. NVIDIA’s H100s sell out months in advance, chip supply chains are under pressure, and AI infrastructure decisions now shape entire industries.

This course provides a complete, practical understanding of how the GPU market works — economically, technologically, and strategically.

Students will learn how GPU demand is formed, what drives pricing, how the semiconductor supply chain operates, and how major players (NVIDIA, AMD, cloud providers) shape the AI ecosystem.

This is not a hardware engineering course — it’s a market, economic, and business-oriented deep dive for people who want to understand how GPU-based AI infrastructure really works.

🎯 Who This Course Is For

This course is designed for:

• AI/ML practitioners who want to understand the business side of GPUs

• Product managers working with AI or cloud-based systems

• Startup founders building AI products

• Tech analysts, researchers, and consultants

• Investors or non-technical business professionals who want to understand the fundamentals of GPU-driven markets

• Anyone curious about how NVIDIA became the backbone of modern AI

No prior economics knowledge is required.

🎓 What You Will Learn

Module 1 — Introduction to GPU Economics

• Why GPUs became central to AI development

• GPU vs CPU economics

• Key performance metrics that influence value

• The “compute scarcity” problem in modern AI

Module 2 — The AI Hardware Market Structure

• The role of NVIDIA, AMD, Intel, and emerging competitors

• Understanding product tiers (L40, A100, H100, GH200, etc.)

• How GPUs are priced and why demand often exceeds supply

• Cloud provider dynamics: AWS, GCP, Azure, Oracle

Module 3 — Semiconductor Supply Chains

• How chips are manufactured: foundries, packaging, and logistics

• TSMC’s role in the GPU ecosystem

• Bottlenecks in advanced process nodes

• Why supply chain constraints directly affect GPU pricing

Module 4 — GPU Allocation, Shortages & Market Cycles

• Why GPUs run out of stock

• How hyperscalers secure long-term GPU contracts

• The impact of global trade, export controls, and market disruptions

• Case studies: 2023–2025 AI GPU shortages

Module 5 — The Economics of AI Clusters

• How companies calculate the ROI of GPU infrastructure

• GPU vs cloud consumption models

• CapEx vs OpEx considerations

• Cost of training vs cost of inference

Module 6 — Understanding NVIDIA’s Business Model

• How NVIDIA makes money

• The CUDA ecosystem advantage

• Data center revenue, margins, and growth drivers

• Why competitors struggle to catch up

Module 7 — Forecasting the Future of AI Hardware

• Trends shaping the next 5 years

• The rise of specialized accelerators

• Potential disruptions in the hardware market

• Predictions for GPU pricing, availability, and competition

🧩 Course Format

• 8 modules

• Video lectures + reading materials

• Case studies based on real-world GPU market events

• Market breakdowns (NVIDIA, AMD, supply chains)

• Optional homework tasks and analytical exercises

• Final project: GPU market analysis report

📈 What You Will Be Able to Do After the Course

By the end of this course, students will be able to:

• Understand how the global GPU market actually works

• Explain why GPU scarcity happens and how it affects AI development

• Analyze the strategies of NVIDIA and other major companies

• Build informed GPU infrastructure decisions

• Evaluate AI hardware pricing and cost structures

• Predict market shifts and demand cycles

• Speak confidently about the economics of AI hardware

📜 Certificate

Upon completion, students receive a Certificate in GPU Economics & AI Hardware Fundamentals.