Modern AI Engineering

Lesson 17.1 · 29 min · video lesson

GPUs for Deep Learning: Parallelism at the Core

Why does a chip designed to draw video-game pixels train and run almost every modern AI model — and why does the size of its memory matter as much as its speed?

In short: A GPU is a processor with thousands of simple cores that all perform the same kind of arithmetic at once, plus very fast memory to feed them. Deep learning is mostly huge matrix multiplications made of millions of independent multiply-adds, which is exactly the kind of work a GPU parallelises well. Specialised Tensor Cores, low-precision number formats and the CUDA software stack make it faster still, while VRAM size and memory bandwidth decide which models fit and how quickly they generate tokens.

What you will learn

  • What is a GPU?
  • Why is the GPU perfect for deep learning?
  • CPU vs GPU
  • The math professor and the thousands of students
  • Why deep learning is mostly matrix multiplication
  • Serial work vs parallel work
  • GPU memory (VRAM) and memory bandwidth
  • Why the model must fit in VRAM
  • Tensor Cores and lower precision (FP16, BF16, INT8)
  • CUDA and the software stack (cuDNN)
  • Training vs inference on GPUs
  • Multiple GPUs working together
  • Why NVIDIA GPUs power modern AI

In this lesson

  1. What is a GPU?
  2. The math professor and the thousands of students
  3. CPU vs GPU
  4. Why deep learning is mostly matrix multiplication
  5. Serial work vs parallel work
  6. GPU memory (VRAM) and memory bandwidth
  7. Why the model must fit in VRAM
  8. Tensor Cores and lower precision (FP16, BF16, INT8)
  9. CUDA and the software stack (cuDNN)
  10. Training vs inference on GPUs
  11. Multiple GPUs working together
  12. Why NVIDIA GPUs power modern AI
  13. Worked example, step by step
  14. Practice: try it yourself

Key terms in this lesson

GPU
A processor with thousands of simple cores designed for parallel, throughput-oriented computation.
VRAM / HBM
The GPU's own memory; HBM is high-bandwidth memory stacked beside the chip.
Memory bandwidth
How many bytes per second can move between memory and the compute units.
Arithmetic intensity
FLOPs performed per byte moved from memory; decides compute-bound vs memory-bound.
Tensor Core
A GPU unit that multiplies small matrix tiles in one operation in reduced precision.
Mixed precision
Training with low-precision maths while keeping key values (master weights) in FP32.
cuDNN
NVIDIA's library of optimised deep-learning operations built on CUDA.

This lesson is part of the Generative AI Engineering and Complete AI Engineer tracks. See plans and pricing to open the full lesson, its video, labs and quiz, or try the free lesson first (free account needed).

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