Keysight Technologies, Inc. has entered the artificial intelligence (AI) and machine learning (ML) infrastructure ecosystem with the introduction of 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.
The deployment and use of AI is growing rapidly across all industrial segments, and the race to train and deliver new AI models quickly and efficiently is a top priority for corporations. AI / ML workloads process vast amounts of data which requires high networking bandwidth and computing performance to reduce training time. However, the cost to design and validate large-scale “what-if” scenario assessments is prohibitive even for the largest AI operators.
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.
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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.
SOURCE: BusinessWire