Keysight launches AI Data Center Builder tool for integrated network and host optimization.

Keysight introduces Keysight AI (KAI) Data Center Builder, an advanced software suite that emulates real-world workloads to evaluate how new algorithms, components, and protocols impact the performance of AI training. The solution integrates large language model (LLM) and other artificial intelligence (AI) model training workloads into the design and validation of AI infrastructure components—networks, hosts, and accelerators—enabling tighter synergy between hardware design, protocols, architectures, and AI training algorithms to boost system performance.

AI operators use various parallel processing strategies, known as model partitioning, to accelerate AI model training. Aligning model partitioning with AI cluster topology and configuration enhances training performance, but critical design questions are best answered through experimentation. Key considerations include scale-up design of GPU interconnects inside an AI host or rack, scale-out network design including bandwidth per GPU and topology, configuration of network load balancing and congestion control, and tuning of the training framework parameters. The KAI Data Center Builder reproduces network communication patterns of real-world AI training jobs to accelerate experimentation and reduce the learning curve necessary for proficiency. Keysight customers can access a library of LLM workloads like GPT and Llama, with a selection of popular model partitioning schemas including Data Parallel (DP), Fully Sharded Data Parallel (FSDP), and three-dimensional (3D) parallelism.

Using the workload emulation application in the KAI Data Center Builder enables AI operators to experiment with parallelism parameters including partition sizes and their distribution over available AI infrastructure, understand the impact of communications within and among partitions on overall job completion time (JCT), identify low-performing collective operations and drill down to identify bottlenecks, and analyze network utilization, tail latency, and congestion to understand their impact on JCT. The solution enables AI operators, GPU cloud providers, and infrastructure vendors to bring realistic AI workloads into their lab setups to validate evolving designs of AI clusters and new components, while also experimenting to fine-tune model partitioning schemas, parameters, and algorithms to optimize infrastructure and improve AI workload performance.

KAI Data Center Builder is the foundation of the Keysight Artificial Intelligence (KAI) architecture, a portfolio of end-to-end solutions designed to help customers scale artificial intelligence processing capacity in data centers by validating AI cluster components using real-world AI workload emulation. Keysight will showcase KAI Data Center Builder and its workload emulation capabilities at booth #1301 at the OFC 2025 conference, April 1–3, at the Moscone Center in San Francisco, California. According to Ram Periakaruppan, Vice President and General Manager, Network Test & Security Solutions at Keysight, "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. KAI Data Center Builder's workload emulation brings a new level of realism to AI component and system design, optimizing workloads for peak performance."