4D Training & Consultancy

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.

Duration confirmed during proposalIn-house, online, or customized deliveryCorporate teams and professional groups

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.

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.

Program outline

Outline points are grouped in one designed block instead of being treated as separate module cards.

Module 1: DGX Platform Architecture

GPU, NVLink, NVSwitch, CPU, memory, storage, and management components

DGX OS, drivers, firmware, and NVIDIA software stack

Rack power, cooling, cabling, and deployment prerequisites

Module 2: Fabric and Storage Integration

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

Module 3: System Provisioning and Resource Control

Base Command Manager concepts, images, nodes, and health checks

Containers, Slurm or Kubernetes scheduling, and GPU allocation

MIG profiles, isolation, quotas, and workload priorities

Module 4: Monitoring and Failure Handling

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

Module 5: DGX Operations Workshop

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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