Key AI Data Center Challenges

Dive into key challenges in AI data centers: scalability, network, power efficiency, and more.

A Q&A with Alex Bortok on the Keysight AI Data Center Builder

Keysight’s Alex Bortok explores how AI is transforming data center design. As generative AI models surge, two major challenges have emerged. First, a global shortage of GPUs made it nearly impossible to build test labs. Second, the AI ecosystem — models, software, hardware — was changing so fast, no static solution could keep up. In this interview video, you will learn how Keysight AI (KAI) Data Center Builder emulates real-world AI workloads, enabling the design and validation of next-gen data center networks without expensive GPUs.

Keysight AI Data Center Builder

Design, validate and benchmark AI data center infrastructure with emulated AI workloads.

In-Depth: Keysight AI

In this video interview, Ram Periakaruppan, VP and GM of Network Applications and Security product at Keysight Technologies, discusses the multifaceted challenges facing AI data centers today — covering issues like scalability and system efficiency when running complex AI models. He also shares actionable strategies to optimize AI performance and efficiency using the Keysight AI portfolio of AI-ready data center solutions.

Executive Perspective: Keysight AI

Listen to Ram Periakaruppan, Vice President and General Manager of Network Applications and Security business at Keysight Technologies, discuss key challenges facing AI data centers, how to optimize AI performance and efficiency, and how Keysight’s helping with the Keysight AI portfolio of AI-ready data center solutions.

Scaling AI Data Centers

Scaling AI Data Centers explains why AI-scale computing pushes every part of the data center, and why removing one constraint often exposes the next. It presents a chip-to-cluster framework spanning pre-silicon design, wafers, chips and chiplets, boards, servers, racks, full data centers, and edge deployments. The document positions Keysight Technologies strategic advantage as breadth plus correlation: predict system behavior before hardware exists, map physical margins at the wafer and photonics layer, then validate interconnects and workloads under conditions that resemble production. Across the stack it highlights solution areas such as electronic design automation for chiplet interconnect and photonic design, automated silicon and silicon photonics wafer test, post-silicon validation for die-to-die standards like UCIe, and board-level signal, power integrity, and electromagnetic interference debugging. It then moves into server and rack validation with protocol analysis, 800G and 1.6T Ethernet transceiver workflows, high-scale traffic generation for training and inference patterns, and Ethernet transport validation for RoCEv2 and Ultra Ethernet. At data center and edge scale it adds emulation, security and performance testing, coherent optical transport analysis, network visibility, and wireless and AI-RAN validation. It concludes that multi-layer challenges demand multi-level expertise, pairing physics-based insight with system context to help teams stay confidently generations ahead.

How do AI/ML data centers differ from traditional ones?

Keysight answers frequently asked questions about AI, data centers, and 6G. In this video, learn how AI data centers differ from other data centers.

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.

Keysight AI: KAI Data Center Builder Overview

Explore the Keysight AI (KAI) Data Center Builder. Keysight AI Data Center Builder emulates real-world AI workloads and traffic profiles, helping you validate network architectures, optimize component- and system-level performance, and fix problems proactively.

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.

Overview: Collective Benchmarking for AI Data Centers

Discover why network performance is so crucial in AI training — as well as how Keysight AI Data Center Builder can help co-tune the software stack and network components to achieve optimal performance.

Hands-On Demo: Collective Benchmarking for AI Data Centers

Watch a demo of Keysight AI (KAI) Data Center Builder, showcasing how different variables impact AI network efficiency and performance.

Keysight AI Data Center Builder

This solution brief introduces how Keysight AI (KAI) Data Center Builder addresses these challenges by accelerating AI / ML networks and input/output innovation cycles. This innovative software uses high-density traffic load appliances that support 100-800GE ports to emulate AI hosts and realistic AI / ML traffic patterns. You will learn how KAI Data Center Builder can help speed up releases, lower costs, simplify setup and maintenance, and improve overall performance.

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.

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.

800G / 1.6T Data Center Transceiver Test

This white paper delves into AI data center trends, addressing how hyperscale data center architects and operators must scale networks from 400G to 800G / 1.6 terabit (1.6T) Ethernet to meet these demands. This guide explores key strategies to increase interconnect bandwidth, ensure interoperability and reliability, and reduce test time, power, and cost.

AI Data Centers

Building networks capable of handling the demands of AI workloads means validating every component, connection, and configuration. With the stakes and scale this high, even the smallest efficiency gain, operational improvement, or performance enhancement can unlock significant returns — reducing disruptions and preventing cascading failures.

Keysight AI (KAI) Data Center Builder Demo

This demo shows how Keysight’s AI Data Center Builder emulates real-world AI training workloads to validate AI infrastructure performance at scale. See how full-stack workload emulation enables tighter synergy between hardware design, protocols, architectures, and AI training algorithms, boosting system performance.

AI Data Center Power

Maximize AI data center power efficiency to improve AI workload scaling. Unlock the full potential of your AI data center with Keysight AI solutions.

Riding the AI Data Center Growth Curve

In this eBook, Lightwave examines how data center providers are leveraging new technologies that not only enable them to scale bandwidth but also increase energy efficiency in the age of artificial intelligence.