Improving Scalability in AI Data Center Clusters

The rise of artificial intelligence (AI) is fueling a worldwide race to build larger and more powerful data centers. A recent Cisco survey revealed that 89% of respondents plan to deploy some form of AI-ready data center clusters by 2026. As companies and nations push their boundaries of AI innovations, network engineers and architects face a pivotal challenge: ensuring that existing network infrastructures can support the increasingly complex workloads demanded by AI.

At the heart of this transformation lies the AI clusters. These clusters are essentially mini networks composed of thousands—or even hundreds of thousands—of GPUs working in unison to train AI models. Scaling these clusters introduces several key challenges. First, the network infrastructure must handle the enormous volume of traffic generated by AI workloads. When clusters expand to include tens of thousands of GPUs, even minor network delays can result in prolonged job completion times and inefficient resource utilization. Second, upgrading to high-speed interconnects like 400G, 800G, or even 1.6T systems is essential, but these upgrades come with their own technical and financial hurdles, including the need for precision tuning to ensure optimal performance and reliability. Financial factors also play a critical role in scaling AI clusters. Beyond the high cost of GPUs, organizations must invest heavily in power, cooling, and networking equipment.

This is where advanced emulation solutions, like the Keysight AI Data Center Builder, make a significant impact. By accurately emulating realistic, high-traffic AI workloads, these tools allow engineers to fine-tune network configurations and validate protocols before deployment. Furthermore, the white paper highlights emerging standards, such as the Ultra Ethernet Consortium (UEC). As AI continues to transform data center industries, embracing advanced emulation solutions will be crucial for building scalable, future-ready AI data centers.