Keysight AI (KAI) Data Center Builder emulates AI workloads without requiring large GPU clusters, enabling evaluation of how new algorithms, components, and protocols affect AI training. The software suite integrates large language model (LLM) and other AI model workloads into the design and validation of AI infrastructure components, including networks, hosts, and accelerators.
KAI Data Center Builder simulates real-world AI training network patterns to speed experimentation, reduce the learning curve, and identify performance degradation causes that real jobs may not reveal. Keysight customers can access LLM workloads like GPT and Llama, along with popular model partitioning schemas, such as Data Parallel (DP), Fully Sharded Data Parallel (FSDP), and 3D parallelism.
The KAI Data Center Builder workload emulation application allows AI operators to:
- Experiment with parallelism parameters, including partition sizes and distribution across AI infrastructure (scheduling)
- Assess the impact of communications within and between partitions on overall job completion time (JCT)
- Identify low-performing collective operations and pinpoint bottlenecks
- Analyze network utilization, tail latency, and congestion to understand their effect on JCT
For more information on the KAI Data Center Builder, or to request a demo or price quote, click the product page link below.
KAI Data Center Builder product page
Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.
The post Software optimizes AI infrastructure performance appeared first on EDN.