Course

Lesson 1 - Introduction to AI/ML Data Center Networks

This AI networking bootcamp explores the differences between machine learning and inference in data centers, highlighting GPU performance, network bottlenecks, workflow failures, and scaling challenges. It emphasizes optimizing latency, reliability, and infrastructure to support high-performance AI workloads effectively.

White Papers

Lesson 2 - How AI / ML Networks Differ from Traditional Networks

Artificial intelligence (AI) and machine learning (ML) are driving big changes in data center networks — including new requirements, traffic patterns, and technologies. Discover how Keysight solutions help optimize AI networks.

Lesson 3 - Maximizing AI/ML Back-End Performance

Keysight’s KAI Data Center Builder optimizes AI data center design by emulating GPU workloads, benchmarking backend network performance, and identifying bottlenecks. It enables scalable, cost-effective testing of AI training and inference infrastructure without requiring physical GPUs in the lab.

White Papers

Lesson 4 - Benchmarking Collective Operations

Network performance benchmarking plays a vital role in identifying opportunities to improve overall throughput without additional hardware costs. Benchmarking of collective operations is a foundational methodology for understanding the performance limits of distributed AI infrastructure. This white paper explains the operation of AI collectives, defines terminology, and reviews the most common metrics associated with benchmarking AI networks.

White Papers

Lesson 5 - Improving Scalability in AI Data Center Clusters

As AI models grow more complex and larger, effectively scaling data center clusters has evolved into a mission-critical priority for organizations aiming to stay competitive. This white paper explores the rise of AI clusters, highlights the key scaling challenges — particularly within network infrastructure — and demonstrates how advanced emulation solutions can help you overcome these obstacles.

Lessons

Lesson 6 - Demonstrating Keysight’s AI Fabric Test Methodology

This session showcases Keysight's AI fabric test methodology using KAI Data Center Builder. It covers key concepts like topology design, congestion control, and performance metrics, demonstrating how automated testing and configuration tuning optimize AI network performance. A live demo highlights the benefits of congestion control in maximizing bandwidth and efficiency.

Application Notes

Lesson 7 - AI Fabric Test Methodology

In the context of Artificial Intelligence (AI) systems, particularly Large Language Models (LLMs), a network fabric refers to the underlying infrastructure that connects and enables communication between various components or nodes within the system. The network fabric is crucial in facilitating efficient and effective AI system operation, including LLM training.

Lesson 8 - Introduction to Validating AI Data Center Front-End Networks

Keysight’s Cyperf enables secure, low-latency testing of front-end AI data center networks, ensuring efficient data ingestion and high-speed communication. It validates bandwidth, latency, and security, optimizing infrastructure to support scalable, high-performance large language model training.

Lessons

Lesson 9 - Validating Front-end AI Networks

LM training, highlighting Keysight’s CyPerf for testing performance, scalability, and security across AI data center networks.

Application Notes

Lesson 10 - High-Performance Networking Offloads for AI / ML-Focused Cloud Platforms

An approach to validating and testing the performance of SmartNIC, underlying LLM infrastructure, and various networking components

Lesson 11 - AI Data Center Networking Case Studies and Strategies

In this final session, customers share real-world use cases of Keysight tools covered throughout the bootcamp. The program concludes with executive Ram Periakaruppan offering a comprehensive overview of Keysight’s AI Data Center solutions spanning Compute, Interconnect, Networking, and Power infrastructure.

Case Studies

Lesson 12 - Success Story: Building Next-Generation AI Networks

Keysight helps Juniper validate next generation network fabric by emulating collective communications workload coming from a large scale of AI accelerators.

Reports

Lesson 13 - Tolly Test Report: Dell PowerEdge and Broadcom AI Performance Benchmarking

Artificial Intelligence (AI) places new and unprecedented demands on servers and the network fabric. During the AI training process, massive amounts of data need to be exchanged among servers. Dell commissioned Tolly to benchmark the IPv6 RDMA offload performance of the Broadcom 57508 Dual-Port 100 Gb/s Ethernet Controller (LAN adapter) running in the Dell PowerEdge R760xa Rack Servers by testing with Keysight AI benchmarks.

eBooks

Lesson 14 - 5 Key Strategies to Optimize and Scale AI Data Centers

The eBook explores scaling AI data centers, optimizing GPUs, high-speed interconnects, power efficiency, and network reliability. It highlights Keysight AI (KAI) solutions for validation, benchmarking, and optimization.

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