How AI / ML Networks Differ from Traditional Networks
White Papers
How Artificial Intelligence (AI) and Machine Learning (ML) Networks Differ from Traditional Networks
The increasing adoption of artificial intelligence (AI) and machine learning (ML) demands more robust and efficient data center networks. Discover the new requirements for AI networks, the distinctiveness of AI and ML traffic patterns, the technology available to make Ethernet networks suitable to run high-performance AI workloads, and how Keysight solutions can help optimize AI networks.
This white paper will teach you everything you need to know about how artificial intelligence and machine learning impact data center networking and design — answering all these questions and more.
- How do networks that support artificial intelligence and machine learning models differ from traditional data center networks?
- What is an AI cluster, how does it work, and how does it scale?
- How are large language models (LLMs) built and trained?
- How do performance bottlenecks, such as packet latency and packet loss impact GPU utilization?
- What kinds of new traffic patterns and workloads do AI- and ML-optimized data centers need to be ready for?
- How do you measure AI network performance?
- How do you optimize a network with nearly double the average link utilization of a traditional data center?
- Can you configure Ethernet networks for AI / ML to avoid congestion and optimize GPU performance?
- How do you benchmark an AI network?
- Is it possible to emulate AI / ML workloads, AI cluster communications, and GPU behavior?