Optimize data center networking for AI workloads [London – United Kingdom]

Author: DCD | Source: Nokia | Read the full article

In today's world, artificial intelligence (AI) is transforming how businesses operate across various industries. However, to fully harness the power of AI, organizations face significant challenges, particularly in ensuring their data centers can manage the large amounts of data and computing power that AI requires. This article discusses the importance of optimizing data center networking to support AI workloads effectively.

AI workloads can be divided into two main types: training and inference. Training involves preparing AI models using vast amounts of data, which demands high-speed and reliable network connections. In contrast, inference is about using these trained models to provide insights or predictions, requiring quick responses from the network. Both types of workloads have unique networking needs that organizations must address to improve performance and efficiency.

To meet these demands, companies are increasingly turning to Ethernet technology for their data center networks. This shift is supported by advancements that enhance Ethernet's capabilities, making it suitable for AI applications. The article emphasizes the need for flexible hardware, modern network operating systems, and automation tools to create a robust infrastructure that can handle the complexities of AI workloads.

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