Keysight Launches AI Inference Emulation Platform

Keysight Technologies, at NVIDIA GTC in San Jose, announced Keysight AI Inference Builder (KAI Inference Builder), an emulation and analytics platform designed to validate inference-optimized AI infrastructure at scale. Keysight demonstrated the solution at the NVIDIA GTC conference, showcasing operation within NVIDIA DSX Air AI factory simulation environments to model and optimize AI data center infrastructure, architectures, and performance. 

According to the company, traditional testing methods like synthetic traffic generation or GPU benchmarks cannot accurately reproduce the latency-sensitive workload behavior of AI inferencing across compute, networking, memory, storage, and security layers. KAI Inference Builder solves this problem by recreating realistic inference workload patterns and modeling industry-specific usage patterns to validate AI infrastructure, applications, and data center deployments. The platform gives AI cloud providers, hardware vendors, and application developers a scalable solution for measuring, validating, and optimizing real-world inference performance. 

According to the company, key benefits include: 

• Built for the Inference Era: As part of the Keysight Artificial Intelligence (KAI) portfolio, KAI Inference Builder emulates AI inference workloads at scale and validates full-stack deployments under real-world conditions to optimize performance, scale, and security. 

• Industry- and Application-Specific Benchmarking: Instead of generic emulations, KAI Inference Builder emulates industry-specific usage patterns and LLM architectures for AI models seen in finance, healthcare, and other verticals, enabling organizations to model and analyze infrastructure and application behavior across different types of AI data center deployments. 

• End-to-End Validation and Optimization: The solution evaluates inference workflows from user request to model response, helping teams reduce costly rework by identifying and resolving bottlenecks early across compute, network, and security layers. 

• Subsystem Isolation and Root-Cause Precision: The solution can also do client-only emulation, which identifies where performance bottlenecks emerge across the AI infrastructure stack under load, enabling targeted optimization that reduces overprovisioning, lowers costs, and improves overall efficiency. 

“Inference is the key to unlocking AI’s ROI, but that can be challenging to achieve when system resources aren’t optimized for capacity and performance," said Ram Periakaruppan, Vice President and General Manager, Network Test & Security Solutions at Keysight. "KAI Inference Builder provides visibility into real-world inference performance across the full stack, enabling customers to validate and optimize deployments before hardware reaches the rack. Showcasing this capability at NVIDIA GTC using NVIDIA’s Air platform demonstrates how organizations can accelerate the path to production while reducing risk and cost.” 

Sources: Press materials received from the company and additional information gleaned from the company’s website.