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Keysight AI Data Center Test Platform Introduced

Keysight Technologies introduced the Keysight AI Data Center Test Platform, designed to accelerate innovation in AI / ML network validation and optimization.

The solution significantly improves benchmarking of new AI infrastructures with unprecedented scale and efficiency.

To overcome this challenge and accelerate the design and testing of AI / ML infrastructure, the Keysight AI Data Center Test Platform delivers highly tunable AI workload emulation, pre-packaged benchmarking apps, and dataset analysis tools to significantly improve performance of the AI / ML cluster network fabric.

To accelerate AI / ML network design, Keysight’s solution for data centers:

- Emulates high-scale AI workloads with measurable fidelity – Offers deep insights into collective communication performance

- Simplifies the benchmarking process – Provides validation of AI network fabric with pre-packaged benchmark applications, built through partnerships with the largest AI operators and AI infrastructure vendors

- Executes defined AI / ML behavioral models – Enables sharing between users and customers to help reproduce experiments

- Offers a choice of test engines – Choose between AI workload emulation on Keysight hardware load appliances and software endpoints or real AI accelerators to compare benchmarking results

The Keysight platform enables large scale validation and experimentation with fabric design in a realistic and cost-effective way. This solution complements testing AI / ML workloads using GPUs, providing AI operators with a more scalable, robust, and integrated AI test platform.

Ram Periakaruppan, VP and GM, Network Test & Security Solutions, Keysight, said: “As a leader in testing ultra-high-speed 800G Ethernet networks, Keysight continues to lead the way in innovation, having formed close partnerships with hyperscalers to co-design this groundbreaking new AI test platform that realistically benchmarks and emulates high-scale AI workloads unlike other test tools available today.”

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Keysight AI Data Center Test Platform Introduced

Keysight Technologies introduced the Keysight AI Data Center Test Platform, designed to accelerate innovation in AI / ML network validation and optimization.

The solution significantly improves benchmarking of new AI infrastructures with unprecedented scale and efficiency.

To overcome this challenge and accelerate the design and testing of AI / ML infrastructure, the Keysight AI Data Center Test Platform delivers highly tunable AI workload emulation, pre-packaged benchmarking apps, and dataset analysis tools to significantly improve performance of the AI / ML cluster network fabric.

To accelerate AI / ML network design, Keysight’s solution for data centers:

- Emulates high-scale AI workloads with measurable fidelity – Offers deep insights into collective communication performance

- Simplifies the benchmarking process – Provides validation of AI network fabric with pre-packaged benchmark applications, built through partnerships with the largest AI operators and AI infrastructure vendors

- Executes defined AI / ML behavioral models – Enables sharing between users and customers to help reproduce experiments

- Offers a choice of test engines – Choose between AI workload emulation on Keysight hardware load appliances and software endpoints or real AI accelerators to compare benchmarking results

The Keysight platform enables large scale validation and experimentation with fabric design in a realistic and cost-effective way. This solution complements testing AI / ML workloads using GPUs, providing AI operators with a more scalable, robust, and integrated AI test platform.

Ram Periakaruppan, VP and GM, Network Test & Security Solutions, Keysight, said: “As a leader in testing ultra-high-speed 800G Ethernet networks, Keysight continues to lead the way in innovation, having formed close partnerships with hyperscalers to co-design this groundbreaking new AI test platform that realistically benchmarks and emulates high-scale AI workloads unlike other test tools available today.”

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Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...