Ten HPC cluster labs and four design projects
Every weekend pairs a guided lab with a main lab on a live multi-GPU cluster, and four design projects become the portfolio you walk an interviewer or client through.
Keyboard-level work on real GPUs and fabrics
Lab 1: Meet the GPU Node
Inspect GPUs, memory hierarchy, PCIe root complexes and NVLink topology using nvidia-smi, lspci -tv and topology matrix commands.
Modules 1, 5, 6
Lab 2: Collectives in Action
Run NCCL benchmarks (all_reduce_perf), compare Ring vs Tree efficiency, and launch a distributed PyTorch benchmark across GPUs.
Modules 3, 4
Lab 3: RDMA Basics and Validation
Query RDMA devices with ibv_devinfo, configure Queue Pairs, and run ib_write_bw and ib_read_lat between nodes.
Modules 9, 10
Lab 4: Lossless Ethernet Tuning
Configure PFC, ECN and DCQCN on switches and SuperNICs, and observe flow control under simulated congestion.
Modules 11, 12
Lab 5: Fabric Design Studio
Design a rail-optimized fabric for a 32-node GPU pod: switch ports, spine-leaf oversubscription and cable tables.
Modules 7, 8, 13, 15
Lab 6: Storage Benchmarking
Benchmark parallel file system throughput with FIO, time checkpoint reads and writes, and configure GPUDirect Storage (gdscheck).
Modules 19
Lab 7: Build the Software Stack
Install drivers, CUDA and the NVIDIA Container Toolkit, partition a GPU into MIG slices, and run jobs in NGC containers.
Modules 20
Lab 8: Schedule GPU Jobs
Write sbatch scripts requesting multi-GPU resources, deploy the GPU Operator on Kubernetes, and schedule inference pods.
Modules 21
Lab 9: Monitor the Cluster
Deploy DCGM Exporter, configure Prometheus collection, and build Grafana monitoring views for GPU utilization, power and temperature.
Modules 22
Lab 10: Break-Fix Scenarios
Diagnose live injected faults: failed links, thermal and power throttling, driver mismatches and real XID errors.
Modules 23
The weekend main labs that build your portfolio
Reference designs, exploded BOMs, telemetry reviews and site surveys modelled on real AI factory deployments, so your skills map to real projects.
NVL72 Exploded BOM Sizing
Size an exploded bill of materials for a GB300 NVL72 rack: compute and switch trays, 132 kW power shelves, busbars, CDUs, OOB switches and cabling for a client proposal.
UFM Fabric Telemetry Review
Read a UFM fabric telemetry and BER report, flag unhealthy links and cables, and write the remediation notes an operations team can act on.
32-Node Rail-Optimized Pod Design
Turn the Fabric Design Studio lab into a design document: NIC-to-rail mapping, leaf and spine counts, oversubscription and the cable table.
Pre-Deployment Site Survey
Audit a site before a GPU cluster lands: power feeds and capping, CDU and facility loop readiness, leak detection and the acceptance checklist.
Each lab and design project becomes a story with a real cause, fix and trade-off you can defend in an architecture interview or a client review.
Enrol nowWhat you walk away with, and where you grow
Eight weekends in, you can size, build, run and troubleshoot a GPU cluster, with a design portfolio and runbooks to prove it. Then you keep growing through our community and freelancing network.
Real cluster experience
Hours of keyboard-level work on a live multi-GPU HPC cluster: RDMA benchmarks, NCCL sweeps, Slurm and Kubernetes scheduling, and injected-fault drills.
A design portfolio
An NVL72 rack bill of materials, a 32-node rail-optimized fabric design, a UFM telemetry review and a site survey you can walk an interviewer through.
Runbooks you keep
Session recordings and notes for 9 weeks from the course start date, plus a Git repository of runbooks for cordon, drain, RMA and acceptance testing, ready for your first production cluster.
Grow through our community
Join a global community of engineers. Showcase your work, stay visible to recruiters, and speed up the opportunities that come your way.
Earn through freelancing
Turn your AI infrastructure skills into income. Strong candidates are invited into our freelancing programs to get paid for real work.
Build it, break it, fix it
Join the next weekend cohort and run every lab on your own seat of a live GPU cluster.