AIC-400 · GPU & AI Compute · Advanced
Large-Scale Training & Datacenter Architecture — full syllabus
The thousand-GPU conversation: 3D parallelism, reference architectures, TCO and where the hardware is heading.
Who this course is for
Senior engineers and architects designing large-scale AI infrastructure — hundred-to-thousand-GPU training systems and the datacentre decisions around them.
Prerequisites
- AIC-210 and AIC-230 (or equivalent production experience)
- AIC-300
- Architecture-level thinking: you have operated real clusters
Course outline
Day 1 — Multi-node training at scale
- Training across hundreds to thousands of GPUs
- 3D parallelism: data + tensor + pipeline
- DeepSpeed ZeRO-Infinity
- NVLink domains and NVSwitch fabrics at scale
- Gradient synchronisation: hierarchical all-reduce, bucket sizes
- Fault-tolerant and elastic training; exascale challenges: power, cooling, reliability
- Trillion-parameter training realities
Day 2 — AI infrastructure architecture
- DGX SuperPOD architecture: compute, storage, networking
- AMD MI300X cluster design
- Reference architectures for AI datacentres
- Rack design: power, cooling, cabling
- Network topology: rail-optimized, dragonfly+
- Multi-vendor integration; TCO analysis
- Firmware lifecycle: BIOS/UEFI, BMC, DPU bundles
Day 3 — Emerging technologies and architecture defence
- CXL 3.0 fabrics and disaggregated memory pooling
- Composable infrastructure: GPU/memory/storage disaggregation
- Serverless GPU (KServe, Knative); WASM runtimes
- Edge AI deployment patterns
- Green AI and sustainability
- Capstone: full architecture review
Hands-on labs
- Lab: model the scaling efficiency of a 3D-parallel training configuration and identify where it breaks
- Lab: design a rack-level layout — power, cooling, cabling, network rails — and cost it with a TCO model
- Lab: evaluate a CXL memory-pooling scenario against a real LLM inference memory problem
- Lab: critique a flawed cluster reference architecture (supplied) and produce a defensible redesign
Capstone project
Design a complete AI datacentre deployment for a stated workload (e.g. training a 70B+ model on a fixed budget): compute fabric, network topology, storage tier, fault-tolerance strategy, firmware/lifecycle plan and TCO — then defend it in a simulated architecture review against cost and reliability challenges.
What you leave with
- A structured method for large-scale training architecture
- Rack-to-fabric design experience with real constraints
- A TCO model you can reuse in procurement discussions
- An evidence-based position on CXL/composable/edge trends
- The design vocabulary of DGX SuperPOD-class systems
Upcoming dates
| Dates | Where | Seats | Early bird | Regular | |
|---|---|---|---|---|---|
| 8 Nov – 10 Nov 20263 full days | RiyadhIn person · KAFD Conference Centre | 3 of 14 | SAR 8,100until 9 Oct | ||
| 15 Nov – 17 Nov 20263 full days | Kuwait CityIn person · Al Hamra Tower | 8 of 14 | KWD 670until 16 Oct | ||
| 22 Nov – 24 Nov 20263 full days | MuscatIn person · Knowledge Oasis Muscat | 3 of 14 | OMR 830until 23 Oct | ||
| 22 Nov – 29 Nov 20266 half-days | Gulf bandLive online · 09:00–13:00 GMT+3 | 5 of 20 | US$1,580until 23 Oct | ||
| 23 Nov – 25 Nov 20263 full days | OttawaIn person · Kanata North Tech Park | 8 of 14 | CAD 2,930until 24 Oct | ||
| 30 Nov – 2 Dec 20263 full days | TorontoIn person · MaRS Discovery District | 3 of 14 | CAD 2,930until 31 Oct | ||
| 30 Nov – 7 Dec 20266 half-days | Europe bandLive online · 09:00–13:00 CET | 10 of 20 | US$1,580until 31 Oct | ||
| 7 Dec – 9 Dec 20263 full days | LondonIn person · Shoreditch Works | 8 of 14 | GBP 1,680until 7 Nov | ||
| 7 Dec – 9 Dec 20263 full days | BerlinIn person · Factory Görlitzer Park | 3 of 14 | EUR 1,990until 7 Nov | ||
| 7 Dec – 14 Dec 20266 half-days | Americas bandLive online · 13:00–17:00 ET | 15 of 20 | US$1,580until 7 Nov |
Book a seat, or bring this course to your team
Seats can be reserved online; private delivery runs on-site or live online, adapted to your stack.
Questions about fit or prerequisites? Email hello@kernelsystems.academy. To save this syllabus, print this page to PDF from your browser.