
Explore InfiniBand as the backbone of AI data centers, delivering stable, lossless, low-latency networking, with real-world analogies and a home-lab to connect concepts to behavior for NCP-AIN certification.
Discover InfiniBand architecture layers: upper workload interfaces like NVMe over fabrics, GPU direct RDMA, and IP over InfiniBand, plus transport, network, link, and physical layers enabling zero-copy end-to-end HPC communication.
Analyze how traditional data transfer uses sockets, TCP/IP, NIC, and the operating system, causing CPU overhead and high latency. See how RDMA solves these problems by reducing memory copies.
Discover how RDMA enables zero copy transfer by allowing the source to write directly into destination memory using RDMA verbs, bypassing CPU and kernel copies for faster data transfers.
Explore the queue pair (QP) as the fundamental InfiniBand communication endpoint, detailing how RDMA verbs create QPs, use send and receive queues, DMA data, and completion queues.
Explore GPU direct storage, which bypasses the CPU and system memory to transfer data from NVMe storage directly to the GPU, reducing IO bottlenecks during training.
InfiniBand Deep Dive: Networking for AI Data Centres
Welcome! I'm here to help you truly understand InfiniBand — the high-performance fabric powering the world's most demanding AI and HPC environments.
As AI workloads explode in scale, the network is no longer an afterthought — it is the bottleneck. Slow fabrics mean idle GPUs, longer training times, and wasted investment in expensive compute. This course gives you the deep, practical knowledge to understand, deploy, and troubleshoot the technology at the heart of modern AI data centres.
What you'll learn:
Why traditional Ethernet and TCP/IP fall short for AI workloads — and how InfiniBand solves latency, throughput, and CPU bottleneck challenges
The full InfiniBand architecture — Physical, Link, Network, Transport, and Upper layers — with real-world analogies that make concepts stick
RDMA, Zero-Copy transfers, Queue Pairs, Memory Registration, and GPUDirect RDMA — the core technologies behind high-speed GPU communication
How the Subnet Manager works — LID assignment, topology discovery, routing table programming, and failover with Standby SM
Traffic isolation using Partition Keys (PKey) — configuring Full and Limited membership across multi-tenant AI clusters
Quality of Service (QoS) — assigning Service Levels (SL), mapping to Virtual Lanes (VL), and configuring bandwidth weights in OpenSM
Routing algorithms in depth — MINHOP, UPDN, Fat-Tree, Adaptive Routing — and why Adaptive Routing is critical for elephant flows in AI workloads
Congestion control, Credit-Based Flow Control, credit loops, and how to prevent fabric deadlocks
Fabric monitoring and management at scale using NVIDIA Unified Fabric Manager (UFM), including Cyber-AI and RBAC
Hands-on troubleshooting using ibdiagnet, ibtracert, mlxlink, ibstat, smpquery, and more
This course is packed with visual analogies, architecture diagrams, and practical troubleshooting scenarios that make even the most complex concepts click. Whether you're a network engineer, a cloud infrastructure specialist, or an AI platform team member, this course will give you the edge to design and operate high-performance AI fabrics with confidence.
No InfiniBand experience required — just bring your curiosity and your ambition.
Let's get started!
NOTE - For learners preparing for NCP-AIN Exam
This course covers 40–50% of the NCP-AIN exam domains, with a focused deep dive on InfiniBand. It is a valuable study companion to cover a significant portion of topics for the certification.