- Technology
- Computer Science
- Distributed Systems
- High Performance Computing
- Parallel Computing
- GPU Programming
- CUDA
- Technology
- Cloud Computing
- Amazon Web Services (AWS)
- AWS Networking & Content Delivery
- Amazon Elastic Load Balancer
- Technology
- Computer Science
- Distributed Systems
- High Performance Computing
- Parallel Computing
- GPU Computing
AI Data Center Networks - Design and Infrastructure for Modern Training and Inference Workloads
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Explore modern AI data center network infrastructure design in this 48-minute NANOG conference talk presented by Juniper Networks experts Michal Styszynski and Mahesh Subramaniam. Gain insights into GPU-to-GPU connect training cluster configurations and inference fabric design options essential for contemporary AI workloads. Learn about critical aspects of fabric-level load balancing, ROCEv2/DCQCN implementation, and data center fabric traffic engineering strategies. Benefit from the extensive industry experience of the speakers, who bring over a decade of expertise in data center networking, cloud architecture, and telecommunications infrastructure development. Discover practical approaches to addressing the unique networking requirements of AI-driven data centers while understanding best practices for optimizing network performance in AI computing environments.
Syllabus
AI Data Center Networks
Taught by
NANOG