AI Ecosystem CourseBy Queensly Austin
Module 2

Compute Fundamentals

Introduction Available · Lessons In Development

What you will be able to diagnose

'We need more compute' can mean several things. A system might be short of processing power, memory space, or the ability to move information quickly. It might also be held back by communication, queues, software, electricity, or cooling. Identify the limiting resource before choosing additional equipment.

We will connect the parameters, inference, vectors, matrices, and learning loop from Modules 0–1 to the physical resources that make them possible. Your project is a request-to-compute diagram and a performance dashboard. Work through the units as you go. Unless a dated source is identified, the numerical scenarios are hypothetical examples for learning.

Planned lessons

  1. What A Processor Actually Does
  2. GPUs And Parallel Work
  3. Matrix Operations Connect Learning To Hardware
  4. Bits, Bytes, And Numerical Precision
  5. Quantization And Its Actual Trade-Offs
  6. FLOPS And The Meaning Of A Peak
  7. Memory Capacity: What Must Fit
  8. Memory Bandwidth: What Must Move
  9. Arithmetic Intensity And The Roofline
  10. Training Has More Work Than Forward Prediction
  11. Prefill And Decode Create Different Inference Work
  12. KV Cache And Long-Context Memory
  13. Batching, Concurrency, And Queueing
  14. Speculative Decoding And Why Verification Matters
  15. Communication: Scale-Up, Scale-Out, And Rack Systems
  16. Utilization, Goodput, And Useful Economics
  17. Compare Accelerator Strategies As Complete Systems