Detailed Notes – Graphics Cards, Cloud, and Servers

1️⃣ What a Graphics Card Actually Does

• GPU = Graphics Processing Unit

  Specialized hardware designed to process multiple data tasks simultaneously — ideal for graphics rendering, AI, and high-performance computing.

• CPU vs GPU:

  Feature	CPU (Central Processing Unit)	GPU (Graphics Processing Unit)
  Task Type	Sequential tasks	Parallel tasks
  Cores	Few, powerful cores	Hundreds or thousands of smaller cores
  Ideal Use	General computing, OS management	Graphics, simulation, AI, data processing

• Architecture Basics:

  • GPU Core Array → thousands of mini processors working together.
  • VRAM (Video RAM) → dedicated, high-speed memory for temporary data.
  • Cooling System → fans, heat pipes, or liquid systems maintain performance.
  • Power Supply → high wattage, typically 150–600W for discrete cards.

2️⃣ GPU Use in Local Systems

• In desktops/laptops, GPUs handle:
  • Rendering graphics (games, CAD, 3D modeling).
  • Video editing and encoding.
  • AI processing for creative tools (e.g., Adobe Sensei, Blender AI features).
• Types:
  • Integrated GPU – built into CPU (low cost, lower performance).
  • Dedicated GPU – separate card (higher performance, more power use).

🧩 Example:

Students in Applied Cyber using Blender or TinkerCAD rely on local GPU rendering for real-time modeling.

3️⃣ GPU Use in Servers and the Cloud

A. GPU Virtualization

• Cloud providers (AWS, Google Cloud, Azure) use virtual GPUs (vGPUs) to share GPU power among multiple users.
• Each user’s virtual machine can run GPU-intensive tasks remotely.
• vGPU technology = hardware partitioning + virtualization layer.

B. Data Centers

• GPU clusters are installed in rack-mounted servers.