Lilly Tech Systems
High-Performance Networking for AI
Understand the networking technologies that enable distributed AI training at scale. From InfiniBand fabrics and RDMA to NVLink/NVSwitch interconnects and optimized network topologies, this course covers the networking fundamentals that every AI infrastructure engineer needs to build and operate high-performance GPU clusters.
What You'll Learn
Complete coverage of networking technologies for AI infrastructure.
InfiniBand
Deploy and manage InfiniBand fabrics for ultra-low latency, high-bandwidth GPU cluster networking.
RDMA
Understand Remote Direct Memory Access for zero-copy, kernel-bypass data transfers between GPUs.
NVLink/NVSwitch
Master NVIDIA's GPU-to-GPU interconnects for intra-node high-bandwidth communication.
Network Topology
Design network topologies (fat-tree, dragonfly, rail-optimized) for optimal AI training performance.
Course Lessons
Follow the lessons in order for comprehensive HPC networking knowledge.
1. Introduction
Why networking matters for AI, bandwidth vs latency, and the AI networking stack from physical to application layer.
2. InfiniBand
InfiniBand architecture, HDR/NDR speeds, subnet management, and deployment for AI clusters.
3. RDMA
Remote Direct Memory Access: RoCE vs InfiniBand RDMA, GPUDirect, and NCCL communication libraries.
4. NVLink/NVSwitch
NVIDIA NVLink generations, NVSwitch architecture, and GPU-to-GPU communication optimization.
5. Network Topology
Fat-tree, dragonfly, and rail-optimized topologies for AI clusters with performance trade-offs.
6. Best Practices
Production networking: congestion control, monitoring, troubleshooting, and performance tuning for AI.
Prerequisites
What you need before starting this course.
- Basic understanding of TCP/IP networking and Ethernet
- Familiarity with distributed training concepts (data parallelism, model parallelism)
- Understanding of GPU architecture basics
- Linux system administration experience