Course Overview of AI Networking:

NVIDIA AI networking engineers play a critical role in designing, deploying, and optimizing high-performance AI infrastructure. This expert-level course equips learners with the knowledge and practical skills required to configure NVIDIA networking technologies for AI and machine learning workloads. 

Participants will learn how to build low-latency, high-throughput data center networks, integrate NVIDIA networking with GPUs, servers, and storage, and troubleshoot performance issues. The course combines instructor-led lectures, demos, hands-on labs, and real-world scenarios to prepare learners for enterprise AI networking environments. 

After completing AI Networking, participants will be able to:

  • Explain AI data center networking architecture and scalable AI factory design
  • Configure NVIDIA Spectrum-X switches for low-latency AI networking
  • Deploy and manage NVIDIA InfiniBand fabrics for distributed AI workloads
  • Integrate NVIDIA networking with Kubernetes and cloud-native environments
  • Monitor, troubleshoot, and optimize AI networking performance
  • Automate AI network configuration using NVUE and Ansible

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Key Features of NVIDIA-Certified Professional: AI Networking

  • AI-Focused Networking Curriculum

    • Designed specifically for AI/ML infrastructure and GPU clusters
    • Covers real enterprise AI networking architectures and best practices
    • Focus on high-performance, low-latency networking for distributed training 

  • Hands-On Labs and Real Scenarios 

    • Guided labs across Spectrum-X, InfiniBand, Kubernetes, and automation
    • Practical troubleshooting exercises using industry tools
    • Real-world networking use cases and demos 

  • End-to-End AI Networking Stack Coverage 

    • Datacenterdesign and rail-optimized topologies
    • RoCE, RDMA, congestion control, and telemetry
    • Monitoring, diagnostics, and automation workflows 

  • Kubernetes and Cloud-Native Integration 

    • Deploy NVIDIA Network Operator
    • Manage RDMA and InfiniBand in containerized environments
    • Validate networking for modern AI platforms 

  • Automation for Scalable AI Infrastructure 

    • Use NVUE templates for switch configuration
    • Automate deployments using Ansible playbooks
    • Build repeatable and scalable networking environments 

Who should Attend NVIDIA-Certified Professional: AI Networking?

  • Network engineers and data center engineers
  • AI/ML infrastructure engineers
  • Cloud and DevOps engineers working with AI workloads
  • HPC engineers and platform architects
  • Professionals preparing for NVIDIA AI networking certification

Prerequisites of NVIDIA-Certified Professional: AI Networking

  • Strong understanding of networking fundamentals (TCP/IP, routing, switching)
  • Familiarity with Linux and command-line tools
  • Basic understanding of AI/ML workloads and GPU infrastructure
  • Experience with Kubernetes or automation tools is beneficial

    • Why Choose CloudThat as Your Training Partner for AI Networking?

    • Authorized & Specialized Training 
      • Expertise in cloud, AI, and data center technologies 
      • Real-world enterprise use cases and best practices 
    • Industry-Experienced Trainers 
      • Certified experts with real AI infrastructure experience 
    • Hands-On Learning Approach 
      • Practical labs and real scenarios for skill mastery 
    • Career and Certification Support 
      • Guidance for NVIDIA certification preparation 
      • Resume and interview support 
    • Updated and Industry-Relevant Content 
      • Regularly updated with the latest NVIDIA AI technologies 

    Learning Objective of NVIDIA-Certified Professional: AI Networking

    • After completing this course, learners will be able to: 
    • Explain AI data center networking architecture and scalable AI factory design 
    • Configure NVIDIA Spectrum-X switches for low-latency AI networking
    • Deploy and manage NVIDIA InfiniBand fabrics for distributed AI workloads 
    • Integrate NVIDIA networking with Kubernetes and cloud-native environments 
    • Monitor, troubleshoot, and optimize AI networking performance 
    • Automate AI network configuration using NVUE and Ansible 

    Course Outline of NVIDIA-Certified Professional: AI Networking Download Course Outline

    Topics:

    • AI factory networking architecture and components
    • Rail-optimized topologies for AI workloads
    • GPU-to-GPU communication fundamentals

    Objectives:

    • Describe AI data center networking architecture and components
    • Explain rail-optimized topology design
    • Understand GPU-to-GPU communication for distributed training

    Activities:

    • Instructor lecture
    • Architecture walkthrough demo
    • Quiz and group discussion

    Topics:

    • Spectrum-X configuration for RoCE
    • QoS, ECN, PFC, adaptive routing and telemetry
    • BGP-EVPN multi-tenancy
    • NVIDIA Air simulation, NetQ monitoring, DOCA, SuperNIC

    Objectives:

    • Configure Spectrum-X for low-latency AI networking
    • Enable congestion control and telemetry
    • Diagnose network congestion and packet loss

    Activities:

    • Demos and Hands-on Labs 1 & 2
    • Guided exercises and quiz

    Topics:

    • Initial configuration and high availability
    • Partition keys (PKeys) and QoS
    • Monitoring using UFM

    Objectives:

    • Perform InfiniBand setup and provisioning
    • Configure multi-tenancy and adaptive routing
    • Monitor bandwidth and link status

    Activities:

    • Demo and Hands-on Labs 3 & 4

    Topics:

    • Deploy NVIDIA Network Operator
    • Manage RDMA and InfiniBand in Kubernetes
    • Verify networking integration

    Objectives:

    • Configure Network Operator in clusters
    • Validate AI networking in Kubernetes

    Activities:

    • Demo and Hands-on Lab 5

    Topics:

    • WJH services and UFM diagnostics
    • Interconnect verification
    • InfiniBand troubleshooting commands

    Objectives:

    • Analyze real-time network events
    • Diagnose connectivity and performance issues

    Activities:

    • Troubleshooting scenarios and Hands-on Lab 6

    Topics:

    • NVUE templates
    • Ansible automation
    • VLAN and RoCE automation

    Objectives:

    • Automate network deployment
    • Implement scalable AI networking workflows

    Activities:

    • Automation demo and Hands-on Lab 7

    Certification Details of NVIDIA-Certified Professional: AI Networking

    • Participants receive a CloudThat Course Completion Certificate aligned to NVIDIA AI Networking certification preparation.

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    Course ID: 28386

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    FAQs for NVIDIA-Certified Professional: AI Networking

    Network, cloud, DevOps, and AI infrastructure professionals who want to specialize in AI networking.

    Basic understanding of AI workloads is helpful but not mandatory.

    Spectrum-X, InfiniBand, RoCE, Kubernetes, NetQ, UFM, NVUE, Ansible, and NVIDIA networking tools.

    Yes. Multiple labs cover configuration, monitoring, troubleshooting, and automation.

    AI infrastructure and networking skills are in high demand across cloud, HPC, and enterprise AI environments.

    AI infrastructure engineer, data center network engineer, cloud network engineer, HPC engineer.

    AI networking and HPC networking professionals are among the highest-paid infrastructure roles due to the rapid growth of AI data centers and GPU clusters.

    Yes. The course aligns with NVIDIA AI networking certification preparation.

    Enquire Now