Train your team

AIC-100 · GPU & AI Compute

Foundations for AI Compute

The architecture, operating system and networking groundwork every GPU systems engineer is assumed to have and often does not.

Foundation 3 days in person6 half-days online Max 14 in person

Who this course is for

Engineers moving into GPU/AI infrastructure who keep hitting gaps in the fundamentals — how the CPU, OS and network actually behave underneath the frameworks.

Prerequisites

Basic command-line LinuxSome exposure to a compiled language (C/C++ helpful)No GPU experience required

Course outline

Day 1 — Computer architecture for AI engineers

  • Fetch/decode/execute and where cycles actually go
  • Memory hierarchy: L1/L2/L3/RAM and why it dominates performance
  • Cache organisation, miss patterns and coherency basics
  • SIMD/vector processing (AVX-512, NEON, SVE)
  • NUMA topology on multi-socket platforms

Day 2 — Operating systems for AI engineers

  • Processes, threads and the 1:1 NPTL model
  • CPU scheduling: FIFO, Round Robin, CFS
  • Virtual memory: page tables, TLB, page faults, copy-on-write
  • Swapping, the OOM killer and huge pages (2 MB/1 GB)
  • I/O subsystem: VFS, page cache, block scheduler
  • Concurrency primitives and IPC

Day 3 — Networking for AI clusters

  • TCP/IP stack end to end
  • Flow control and congestion control (CUBIC, BBR)
  • Ethernet switching, VLANs, IP routing
  • Data-centre topologies: spine-leaf and fat-tree
  • QoS (802.1p, DSCP)
  • First look at kernel bypass, RDMA and DPDK

Hands-on labs

Labs follow the academy model — 35% principles, 20% guided investigation, 45% engineering studio. Every claim you make in a lab is backed by a trace, a counter or a measurement you captured yourself. How we teach

  1. Lab: map your machine's topology with lscpu, numactl and /proc — cores, caches, NUMA nodes, PCIe paths
  2. Lab: trace a real process with strace and /proc/pid/maps; observe scheduling decisions with schedtool and chrt
  3. Lab: capture and analyse live traffic with tcpdump/Wireshark; measure latency and bandwidth with iperf3
  4. Lab: measure cache behaviour with hardware performance counters on a memory-bound microbenchmark

Capstone project

A guided audit of a real (or VM) AI server: you produce a one-page topology and configuration report — CPU/NUMA/PCIe layout, OS memory and scheduler settings, network path — identifying the three configuration issues most likely to hurt GPU workload performance, with evidence for each.

What you leave with

  • A hardware/OS/network topology report you can reproduce on any server
  • Working fluency with lscpu, numactl, strace, perf, tcpdump and iperf3
  • A mental model of the stack underneath every GPU framework
  • The vocabulary to read platform specs and whitepapers critically

How it runs

Every course follows the same model: 35% principles, 20% guided investigation, 45% engineering studio. You leave with working code, raw measurements and an evidence-based report — not a certificate of attendance. Read the methodology or see a full sample lesson.

Material is adapted to your kernel version, hardware and workload before a private delivery. For public cohorts, the environment is provided and configured.

Questions

Who is this course for?

Engineers moving into GPU/AI infrastructure who keep hitting gaps in the fundamentals — how the CPU, OS and network actually behave underneath the frameworks. It sits at foundation level within the GPU & AI Compute track.

What do I need to know already?

Specific prerequisites for this course: Basic command-line Linux; Some exposure to a compiled language (C/C++ helpful); No GPU experience required. We confirm levels before the cohort starts and adapt if a group is stronger or weaker than expected.

Can this run privately for my team?

Yes. Any course runs on-site at your offices anywhere, or live online for a distributed team, with labs adapted to your hardware and codebase.

What is the difference between in-person and online?

In person is 3 full days with hardware on your desk, capped at 14. Online is 6 half-day sessions across about two weeks so you can keep working, capped at 20, with remote lab access.

Do you invoice companies?

Yes. Purchase orders are accepted and invoicing is available in USD, EUR, GBP, SAR and CAD.

Upcoming dates

DatesWhereSeatsEarly birdRegular
25 Oct – 27 Oct 20263 full days RiyadhIn person · KAFD Conference Centre 10 of 14 —SAR 6,750
1 Nov – 3 Nov 20263 full days Kuwait CityIn person · Al Hamra Tower 5 of 14 —KWD 560
1 Nov – 3 Nov 20263 full days MuscatIn person · Knowledge Oasis Muscat 10 of 14 —OMR 690
8 Nov – 15 Nov 20266 half-days Gulf bandLive online · 09:00–13:00 GMT+3 18 of 20 US$1,170until 9 OctUS$1,300
9 Nov – 11 Nov 20263 full days OttawaIn person · Kanata North Tech Park 5 of 14 CAD 2,200until 10 OctCAD 2,450
16 Nov – 18 Nov 20263 full days TorontoIn person · MaRS Discovery District 10 of 14 CAD 2,200until 17 OctCAD 2,450
16 Nov – 18 Nov 20263 full days LondonIn person · Shoreditch Works 5 of 14 GBP 1,260until 17 OctGBP 1,400
16 Nov – 23 Nov 20266 half-days Europe bandLive online · 09:00–13:00 CET 7 of 20 US$1,170until 17 OctUS$1,300
16 Nov – 23 Nov 20266 half-days Americas bandLive online · 13:00–17:00 ET 12 of 20 US$1,170until 17 OctUS$1,300
23 Nov – 25 Nov 20263 full days BerlinIn person · Factory Görlitzer Park 10 of 14 EUR 1,490until 24 OctEUR 1,660

Dates shown for the next few months. If nothing fits, tell us where and when — cohorts are added on demand, and private delivery can be scheduled any week.

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