Free Download Course NVIDIA Certified Professional AI Networking (NCP-AIN)
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 11h 3m | Size: 3.92 GB
Concept-Driven NVIDIA AI Data Center Networking - Architecture, Design, Spectrum-X, InfiniBand, Kubernetes, and Operatio
What you'll learn
Understand NVIDIA AI data center networking architecture and how GPUs, DPUs, switches, and storage work together for AI workloads.
Explain AI factory design principles and rail-optimized network topologies for scalable, high-performance NVIDIA environments.
Differentiate NVIDIA Spectrum-X Ethernet and InfiniBand networking concepts for AI training and inference workloads.
Understand GPU-to-GPU communication fundamentals and how network design impacts latency, throughput, and AI performance.
Learn core concepts of QoS, congestion control, telemetry, and observability in NVIDIA AI networking environments.
Understand how Kubernetes integrates with NVIDIA AI networking, including RDMA, InfiniBand, and GPU resource awareness.
Develop architectural reasoning to analyze AI network performance, scalability, and reliability challenges.
Prepare for NVIDIA NCP-AIN certification with strong conceptual clarity and design-focused understanding.
Requirements
This course is designed to build understanding from core networking and AI infrastructure fundamentals.
Basic understanding of computer networking concepts (IP, routing, switching)
General familiarity with data center or cloud infrastructure concepts
Awareness of AI, GPU computing, or machine learning workflows (helpful but not required)
Interest in AI infrastructure, data center design, or high-performance networking
Description
Note: This course contains the use of artificial intelligence Voice (AI Voice). This course does not include Hands On sessions.Strong focus of this course is to Clear certification exam, Understand deep concepts, and have full fundamentals clarity. This course is fast track to save time and in limited time cover entire syllabus as well deep grasping. This course include 2 eBooks, which you can download and go through in detail. NVIDIA Certified Professional AI Networking (NCP-AIN) - Fundamentals-First TrainingModern AI workloads demand purpose-built networking architectures, not traditional data center designs. High-performance GPUs, ultra-low latency fabrics, rail-optimized topologies, congestion-aware routing, and GPU-to-GPU communication patterns have fundamentally changed how AI data centers are designed, optimized, and operated.This course is a concept-driven, architecture-focused learning experience created specifically for professionals preparing for the NVIDIA Certified Professional AI Networking (NCP-AIN) certification and for anyone who wants a deep, structured understanding of NVIDIA AI networking ecosystems-without relying on hands-on labs.Instead of isolated commands or vendor-specific demos, this training focuses on how and why NVIDIA AI networks work, helping you build a clear mental model of AI data center networking from the ground up.What This Course Focuses OnThis course walks you through the entire NVIDIA AI networking landscape, starting from AI data center design fundamentals and moving through Ethernet-based Spectrum-X fabrics, InfiniBand architectures, Kubernetes integration, observability, and automation-purely from a conceptual and design perspective.You will gain clarity on:How NVIDIA AI factories are architectedWhy rail-optimized topologies matter for large-scale GPU clustersHow GPU-to-GPU communication patterns influence network designHow congestion, latency, and throughput are managed in AI fabricsHow NVIDIA Spectrum-X and InfiniBand differ and where each fitsHow DPUs, BlueField, SuperNICs, and telemetry integrate into AI networksHow Kubernetes integrates with RDMA and InfiniBand networkingHow enterprise AI networks are monitored, analyzed, and automatedCore Topics CoveredAI Data Center Design & OptimizationAI factory architecture and core building blocks (GPUs, DPUs, switches, scalable units)Ethernet vs InfiniBand for AI workloadsStorage considerations for AI throughputRail-optimized and scalable AI network topologiesGPU-to-GPU communication fundamentalsNVIDIA Spectrum-X Networking (Conceptual)Spectrum-X architecture and design philosophyQoS, ECN, PFC, telemetry, and congestion management conceptsMicrosegmentation and multi-tenant AI fabrics using BGP-EVPNNetQ, CloudAI Benchmark, and observability fundamentalsUnderstanding WJH® (What Just Happened) telemetry NVIDIA InfiniBand Networking (Conceptual)InfiniBand architecture and fabric componentsSubnet managers, partitions, and PKeysQoS and adaptive routing conceptsRail-optimized InfiniBand designs for AI scalabilityMonitoring with NVIDIA Unified Fabric Manager (UFM) Kubernetes & AI Networking IntegrationNVIDIA Network Operator architectureRDMA and InfiniBand enablement in KubernetesGPU resource awareness and scheduling conceptsNetworking considerations for AI workloads in containerized environments Observability, Troubleshooting & AutomationTelemetry-driven troubleshooting approachesConceptual use of NetQ, UFM, WJH, and diagnostic toolsUnderstanding congestion, packet loss, and latency root causesNVUE templates and Ansible automation conceptsZero-touch and large-scale AI network operationsWho This Course Is ForThis course is ideal for:Network engineers transitioning into AI infrastructureData center architects working on GPU or AI workloadsInfrastructure and platform engineers supporting AI platformsKubernetes professionals working with AI workloadsProfessionals preparing for the NVIDIA NCP-AIN certificationArchitects and technical leaders who want clear fundamentals before implementationNo prior hands-on NVIDIA networking experience is required. The course is designed to build understanding from first principles.Why This Course Is DifferentFundamentals-first, no lab dependencyClear architectural explanations using flows, blocks, and diagramsCertification-aligned without exam crammingEnterprise-grade perspective, not tool memorizationIdeal foundation before advanced or hands-on trainingThis course gives you the confidence to reason about AI networking designs, communicate effectively with architects and vendors, and approach real-world AI data center challenges with clarity.
Who this course is for
Network engineers who want to transition into AI data center and GPU networking roles
Data center and infrastructure architects designing or supporting AI and GPU-based environments
Platform and cloud engineers working with AI workloads and high-performance networking
Kubernetes professionals who want to understand AI networking integration concepts
AI infrastructure, MLOps, and platform teams seeking architecture-level clarity
Technical leads and decision-makers involved in AI data center design and planning
Professionals preparing for the NVIDIA Certified Professional AI Networking (NCP-AIN) certification
Engineers who prefer to understand design principles, trade-offs, and architectures before execution
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