Tool To Test And Improve AI System Design

The software helps test AI training by copying tasks. It shows how changes in setup affect speed, helping teams fix problems before use.

Keysight Technologies, Inc. has introduced KAI Data Center Builder, a new software suite designed to emulate real-world AI training workloads. The tool helps users assess how changes to algorithms, components, and protocols affect the performance of AI systems during the design and validation phases.

KAI Data Center Builder integrates training workloads from large language models (LLMs) and other AI models to evaluate the interaction between hardware components—like networks, hosts, and accelerators—and AI training processes. This helps align hardware design and training algorithms for better overall system performance.

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AI operators often use parallel processing strategies, known as model partitioning, to accelerate training, but the effectiveness of these strategies depends on how well they align with the AI cluster’s design. To make informed decisions, teams must experiment to understand data movement efficiency between GPUs. This includes evaluating how GPU interconnects should be scaled within a host or rack, determining the optimal network topology and bandwidth per GPU, configuring load balancing and congestion control, and tuning the right parameters for the training framework.

KAI’s workload emulation replicates real communication patterns of AI training jobs. This makes it easier to experiment, shortens the learning curve, and provides insight into performance bottlenecks that are hard to pinpoint with real AI workloads alone. Users get access to a library of LLM workloads such as GPT and Llama, and can explore different partitioning strategies like Data Parallel (DP), Fully Sharded Data Parallel (FSDP), and 3D parallelism.

KAI Data Center Builder gives AI operators, cloud providers, and infrastructure vendors a way to test realistic workloads in lab environments. They can validate new AI cluster designs, refine partitioning strategies, and tune performance before full deployment.

Ram Periakaruppan, Vice President and General Manager, Network Test & Security Solutions, Keysight, said: “As AI infrastructure grows in scale and complexity, the need for full-stack validation and optimization becomes crucial. To avoid costly delays and rework, it’s essential to shift validation to earlier phases of the design and manufacturing cycle.”