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Keysight Introduces AI Data Centre Builder

Keysight Technologies introduced Keysight AI (KAI) Data Centre Builder, an advanced software suite that emulates real-world workloads to evaluate how new algorithms, components, and protocols impact the performance of AI training. 

KAI Data Centre Builder’s workload emulation capability 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. This solution enables tighter synergy between hardware design, protocols, architectures, and AI training algorithms, boosting system performance.

The KAI Data Centre Builder workload emulation solution reproduces network communication patterns of real-world AI training jobs to accelerate experimentation, reduce the learning curve necessary for proficiency, and provide deeper insights into the cause of performance degradation, which is challenging to achieve through real AI training jobs alone. Keysight customers can access a library of LLM workloads like GPT and Llama, with a selection of popular model partitioning schemas like Data Parallel (DP), Fully Sharded Data Parallel (FSDP), and three-dimensional (3D) parallelism.

Using the workload emulation application in the KAI Data Centre Builder enables AI operators to:

  • Experiment with parallelism parameters, including partition sizes and their distribution over the available AI infrastructure (scheduling)
  • 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
  • Analyse network utilisation, tail latency, and congestion to understand the impact they have on JCT

The KAI Data Centre Builder's new workload emulation capabilities enable AI operators, GPU cloud providers, and infrastructure vendors to bring realistic AI workloads into their lab setups to validate the evolving designs of AI clusters and new components. They can also experiment to fine-tune model partitioning schemas, parameters, and algorithms to optimise the infrastructure and improve AI workload performance.

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 optimisation 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 Centre Builder’s workload emulation brings a new level of realism to AI component and system design, optimising workloads for peak performance.”

KAI Data Centre 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 centres by validating AI cluster components using real-world AI workload emulation.

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Keysight Introduces AI Data Centre Builder

Keysight Technologies introduced Keysight AI (KAI) Data Centre Builder, an advanced software suite that emulates real-world workloads to evaluate how new algorithms, components, and protocols impact the performance of AI training. 

KAI Data Centre Builder’s workload emulation capability 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. This solution enables tighter synergy between hardware design, protocols, architectures, and AI training algorithms, boosting system performance.

The KAI Data Centre Builder workload emulation solution reproduces network communication patterns of real-world AI training jobs to accelerate experimentation, reduce the learning curve necessary for proficiency, and provide deeper insights into the cause of performance degradation, which is challenging to achieve through real AI training jobs alone. Keysight customers can access a library of LLM workloads like GPT and Llama, with a selection of popular model partitioning schemas like Data Parallel (DP), Fully Sharded Data Parallel (FSDP), and three-dimensional (3D) parallelism.

Using the workload emulation application in the KAI Data Centre Builder enables AI operators to:

  • Experiment with parallelism parameters, including partition sizes and their distribution over the available AI infrastructure (scheduling)
  • 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
  • Analyse network utilisation, tail latency, and congestion to understand the impact they have on JCT

The KAI Data Centre Builder's new workload emulation capabilities enable AI operators, GPU cloud providers, and infrastructure vendors to bring realistic AI workloads into their lab setups to validate the evolving designs of AI clusters and new components. They can also experiment to fine-tune model partitioning schemas, parameters, and algorithms to optimise the infrastructure and improve AI workload performance.

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 optimisation 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 Centre Builder’s workload emulation brings a new level of realism to AI component and system design, optimising workloads for peak performance.”

KAI Data Centre 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 centres by validating AI cluster components using real-world AI workload emulation.

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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