Executive Insights: Tackling AI Infrastructure Bottlenecks

As AI workloads scale rapidly, data centers face increasing pressure to optimize compute, interconnect, and memory. In this exclusive video interview from OFC 2025, Ram Periakaruppan, VP and GM of Keysight’s Network Applications & Security group, outlines the critical challenges confronting AI infrastructure teams — and how Keysight’s KAI Data Center Builder is designed to address them.

From Power Bottlenecks to Inference Scaling: Where the Industry is Headed

The surge in AI compute demand is creating a gridlock — not just in data pipelines, but in literal power availability. As Ram explains, “Power is the biggest challenge in the industry right now.” AI workloads, particularly training large language models (LLMs), are compute-intensive and resource-hungry. But the real shift is happening in AI inference — and with it, a looming memory wall that threatens system scalability.

Organizations must now optimize for inference latency, memory efficiency, and distributed model architectures. The solution? More granular testing, modular deployment, and the ability to emulate and validate designs before production silicon is ready.

Bridging the Gap from Design to Deployment: What Next-Gen AI Solutions Require

As AI infrastructure grows in scale and complexity, next-generation solutions must enable a seamless transition from early-stage design to large-scale deployment. The days of validating only after silicon is available are over. To keep pace, engineering teams must uncover architectural and performance issues before hardware exists and continue validating at scale once deployed in production.

This design-to-deployment continuum is no longer optional — it’s foundational to delivering scalable, efficient AI infrastructure.

Today’s AI systems face a new set of challenges:

• Power availability is emerging as the top constraint for large-scale AI buildouts.
• Inference performance, not just training, is becoming the dominant focus for optimizing latency and user experience.
• The memory wall looms large as models grow and the need for low-latency access intensifies.
• Model parallelism techniques like 3D, 4D, and even 5D data partitioning require flexible infrastructure that can adapt to experimental workloads.

To address these pressures, infrastructure teams need:

• Emulation platforms that can simulate large-scale AI workloads without requiring scarce GPUs
• The ability to experiment early, validating both custom silicon and full-stack system architectures pre-silicon
• Support for proprietary, open, and hybrid architectures, allowing customers to safeguard their IP while optimizing performance
• Consistency across environments, enabling teams to move from lab to deployment without retooling their validation flow

The path forward demands solutions that bridge design and deployment — not just technically, but strategically. From pre-silicon co-design to post-silicon optimization, emulation and validation are becoming central to achieving faster, more reliable, and cost-effective AI scale.

To meet these challenges, Keysight developed the KAI Data Center Builder, a platform purpose-built for AI infrastructure emulation at full scale — without needing access to live GPUs.

By recreating training and inference traffic across AI-native network fabrics, KAI enables engineering teams to:

• Identify root causes of bottlenecks across compute, fabric, and I/O layers
• Emulate proprietary workloads while keeping customer data secure
• Validate custom silicon and system architectures early in the design cycle
• Experiment with 3D, 4D, and 5D model parallelism at full scale

Go Deeper: Beyond the Bottleneck

For a deeper analysis of the trends, architectures, and bottlenecks shaping AI data centers, read Keysight’s new report: Beyond the Bottleneck: AI Cluster Networking Report 2025 Gain insights into AI interconnect challenges, power/cooling constraints, inference scalability, and real-world deployment data from leading hyperscalers and solution providers.

Unlock scalable AI performance today.