Hardware-Networking
NVIDIA DGX and AI Infrastructure Administration
This in-depth course develops directly applicable capability in NVIDIA DGX and AI Infrastructure Administration. It connects DGX Platform Architecture, Fabric and Storage Integration, and System Provisioning and Resource Control to the decisions, controls, and activities participants need to perform in their workplace.
Overview
Practical learning for workplace transfer.
This in-depth course develops directly applicable capability in NVIDIA DGX and AI Infrastructure Administration. It connects DGX Platform Architecture, Fabric and Storage Integration, and System Provisioning and Resource Control to the decisions, controls, and activities participants need to perform in their workplace. The five-module curriculum progresses toward DGX Operations Workshop, using evidence, scenarios, and work products appropriate to the subject.
Prerequisites
Relevant workplace familiarity is recommended; exact technical prerequisites can be adapted to the participant group.
Objectives
- Analyze dgx platform architecture, including gpu, nvlink, nvswitch, cpu, memory, storage, and management components.
- Configure or structure fabric and storage integration, including infiniband and ethernet design for east-west ai traffic.
- Evaluate system provisioning and resource control, including base command manager concepts, images, nodes, and health checks.
- Manage monitoring and failure handling, including dcgm diagnostics, gpu health, fabric telemetry, and thermal events.
- Apply dgx operations workshop, including review rack, network, and storage readiness.
Target audience
- Professionals responsible for this subject area
- Managers, supervisors, and team leaders
- Analysts, specialists, engineers, or coordinators working with the relevant processes
- Project, implementation, assurance, or improvement team members
- Professionals preparing for broader responsibilities in this field
Program outline
A clear structure for the learning journey.
Outline points are grouped in one designed block instead of being treated as separate module cards.
01DGX Platform Architecture3 topics
- GPU, NVLink, NVSwitch, CPU, memory, storage, and management components
- DGX OS, drivers, firmware, and NVIDIA software stack
- Rack power, cooling, cabling, and deployment prerequisites
02Fabric and Storage Integration3 topics
- InfiniBand and Ethernet design for east-west AI traffic
- RDMA, GPUDirect, MTU, QoS, and congestion considerations
- Parallel storage, local NVMe, data staging, and checkpoint traffic
03System Provisioning and Resource Control3 topics
- Base Command Manager concepts, images, nodes, and health checks
- Containers, Slurm or Kubernetes scheduling, and GPU allocation
- MIG profiles, isolation, quotas, and workload priorities
04Monitoring and Failure Handling3 topics
- DCGM diagnostics, GPU health, fabric telemetry, and thermal events
- Xid errors, degraded links, failed GPUs, and node draining
- Firmware and driver upgrade sequencing and recovery
05DGX Operations Workshop3 topics
- Review rack, network, and storage readiness
- Plan workload queues and GPU partitioning
- Diagnose a simulated performance or hardware health incident
Materials provided
- ○ Course-specific presentation slides
- ○ Guided exercises, scenarios, or configured-environment activities appropriate to the subject
- ○ Course-specific worksheets, checklists, or calculation templates
- ○ Applied workplace case materials
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for implementation questions
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy adapts the program to the client’s operating environment. Delivery combines structured explanation with subject-specific analysis, exercises, and implementation decisions so participants can transfer the learning to real responsibilities without implying vendor authorization.
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