
InfiniBand Deep Dive: Networking for AI focused Datacentres — 93% Off Coupon
Learn InfiniBand fundamentals, RDMA, and NVIDIA networking stack to design and optimize AI and HPC infrastructure
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Learning Objectives
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Full Course Description
The official description from Udemy — what's covered, how it's taught, and who it's for:
- 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
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Student Ratings Breakdown
Rated 4.6/5 by 244 students on Udemy. The estimated breakdown:
* Rating distribution is approximated from the aggregate score. Sourced from Udemy. Last verified: July 31, 2026.
Meet Your Instructor
Learn more about Ashish Prajapati, the instructor behind this course on Udemy.
Common Questions
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