Architecture Sizing
Bill-of-materials formulas for DGX, Cisco, and Dell-HPE platforms. Sizing becomes a calculation, not a guess.
DC Tech × AuriLearn · 8 Modules
Master GPU infrastructure. The course built for DC engineers.
This is hands-on training for the engineers who architect, deploy, and operate GPU clusters, delivered by the first ever AI mentor and tutor built that knows the material inwards and out.
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dctech cluster --h100 --nodes 8
ONLINE
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Covers NVIDIA H100 architecture, NVLink, and DGX systems within a vendor-neutral curriculum.
// the infrastructure gap
GPU clusters run on completely different rules.
NVIDIA H100 systems require expertise in NVLink fabrics, HBM memory buses, GPUDirect storage, and RDMA networking. Most training skips all of it. This course doesn't.
40%+
of GPU cluster efficiency is lost to mis-sized hardware, wrong fabric selection, and starved I/O pipelines. Not model code.
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Built exclusively for infrastructure engineers.
Not developers or data scientists. This is for the people responsible for the hardware layer that AI runs on.
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Deep NVIDIA coverage, vendor-neutral principles.
H100, NVLink, GPUDirect, MIG, plus decision frameworks that apply across DGX, Cisco, and Dell-HPE platforms.
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Move from Platform Admin to AI Infrastructure Architect.
Own the architecture review, size clusters accurately, and model 5-year TCO with confidence.
// powered by AuriLearn
Your personalized AI tutor.
On and ready to train you and answer your questions 24/7.
AuriLearn's AI tutor is trained on this NVIDIA curriculum from the ground up: every module, every diagram, every decision framework. Ask anything and get a precise answer right away.
AURILEARN AI TUTOR
Ask anything about GPU infrastructure. Get the right answer immediately.
The AuriLearn AI tutor isn't a generic chatbot. It's trained end-to-end on this course. It knows the difference between InfiniBand and RoCEv2, understands when to use MIG vs. time-slicing, and walks you through a 5-year TCO model step by step.
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Highly personalizedAdapts to your pace, your role, and where your gaps are
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Available 24/73 AM before a deployment? Ask away. No wait time, no queue
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Course-specificFirst of its kind Narrow Vertical Language Model tuned entirely to this GPU infrastructure curriculum - not a general-purpose AI
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Effortless to useAsk or type your question exactly as you would a Senior E
AuriLearn AI Tutor
GPU Course
When should I choose RoCEv2 over InfiniBand for a DGX H100 cluster?
AuriLearn
It depends on scale and existing infrastructure. InfiniBand is the safe choice for large-scale all-to-all training (>16 nodes). Lower latency, simpler to tune. RoCEv2 works well when you already have an Ethernet fabric and can correctly configure PFC/ECN. For a DGX H100 pod under 8 nodes, a well-tuned lossless Ethernet fabric is a legitimate and often cheaper choice. Full decision matrix in Module 4.
What MIG profile works best for multi-tenant inference?
AuriLearn
Ask anything about the GPU infrastructure curriculum…
Always On
No scheduling. No wait time. The AI tutor is available every hour of every day.
Truly Personalized
The AI tutor tracks your module progress and adapts explanations to where you are in the course and what you already know.
Course-Specific Knowledge
Trained specifically on this GPU infrastructure curriculum. It knows every answer to every question on this material.
// the deliverable
You leave with a design document, not just a certificate.
The class ends with a real portfolio-ready AI Infrastructure Design Document that you can bring in to any architecture review.
AI INFRASTRUCTURE DESIGN
Multi-Region GPU Cluster: Reference Design
FabricRoCEv2 · PFC/ECN
5-Year TCOModeled
Fabric Design
Lossless network designs with PFC/ECN, mapped to InfiniBand and RoCEv2 trade-offs for your workload profile.
5-Year TCO Model
A reusable cost model and decision frameworks for sizing, fabric selection, and buy-vs-build trade-offs.
// curriculum
8 modules. Foundation to production.
A practical path from how AI workloads behave at the hardware level to how you scale, secure, and cost-optimize them under real constraints. View full 64-topic data sheet & syllabus →
01 AI Workload Lifecycle & Infrastructure Requirements
Map training, fine-tuning, and inference phases to real compute/storage/network demand — and spot bottlenecks before they hit production.
02 GPU Computing Platforms & Architecture
H100 architecture, NVLink/NVSwitch topology, GPU sizing formulas, and a DGX vs. Cisco vs. Dell-HPE platform comparison with full TCO modeling.
03 Bare-Metal & Kubernetes GPU Orchestration
BIOS/NUMA tuning, CUDA driver management, and NVIDIA GPU Operator — turning raw GPU hardware into reliable, schedulable Kubernetes infrastructure.
04 High-Performance Networking Fabrics
RDMA, GPUDirect, InfiniBand vs. RoCEv2, lossless fabric design, and NCCL — the fabric layer that decides whether distributed training actually scales.
05 GPU Resource Sharing & Multi-Tenant Management
MIG partitioning, RBAC, quota enforcement, and Run:AI/SLURM scheduling for fairly sharing GPU capacity across teams.
06 Storage for AI Workloads
NVMe-oF architecture, VAST/Weka/DDN/NetApp comparisons, and checkpoint strategy for I/O that keeps GPUs fed, not starved.
07 Observability & Operational Excellence
DCGM-to-Grafana monitoring, XID error diagnosis, and incident runbooks for running GPU fleets in production.
08 Hybrid Scaling, Compliance & Cost Optimization
On-prem/cloud/edge placement, GDPR/HIPAA/FedRAMP data residency, and FinOps-driven TCO modeling.
Stack covered
// registration
One price. Everything included.
AuriLearn AI tutor access, 8 progress quizzes, 218 pages of reference material, practitioner certification, and the design deliverable. All in.
LIMITED OFFER
$420
US$5
Market introduction price limited to 50 purchases total · All 8 modules & AuriLearn AI tutor included. Full price after launch: $420.
- 24/7 AuriLearn AI tutor, personalized to this curriculum.
- All 8 class modules
- 218 pages of deep-dive NVIDIA infrastructure reference material
- 8 quiz assessments to track and reinforce your knowledge
- Practitioner certification assessment
- Portfolio-ready AI Infrastructure Design Document
Market introduction price applies to your first registration. By registering you agree to our Terms & Conditions and Refund Policy.
DC Tech × AuriLearn · AI-Powered Infrastructure Training
// faq
Straight answers.
Built for engineers who'd rather read the spec than the brochure.
Who is this course for?
Data center engineers, SREs, platform engineers, and infrastructure architects responsible for building or operating GPU-based AI infrastructure in US enterprises and hyperscalers.
How does the AuriLearn AI tutor work?
AuriLearn's AI tutor is trained specifically on this GPU curriculum, not a general chatbot. Ask questions in plain English and get precise, course-specific answers right away, 24/7. It tracks your progress through each module and adapts to where you are.
What are the prerequisites?
Solid working knowledge of Linux, Kubernetes, and enterprise data center basics. This program is built for experienced infrastructure professionals, not beginners.
Is there a certification?
Yes. A DC Tech AI Infrastructure Practitioner certification, awarded on successful completion of the case-study assessment and verified through AuriLearn's credential system.
Is the curriculum tied to one vendor?
No. This is an independent, vendor-neutral program: not affiliated with or endorsed by NVIDIA Corporation. It covers NVIDIA H100/NVLink in depth alongside decision frameworks for DGX, Cisco, and Dell-HPE, so what you learn applies across the platforms used in real US enterprise data centers.
How is the certification verified?
A shareable digital credential with a verification link, issued through AuriLearn upon passing the case-study assessment.