The NVIDIA H200 GPU has numerous regulatory approvals, which are essential for safe, legal, and reliable deployment of AI and high-performance computing (HPC) workloads globally. These certifications confirm that the hardware meets established international standards for electrical safety, electromagnetic compatibility (EMC), and environmental protection in key regions.

Key approvals include FCC (United States), CE (European Union), ICES (Canada), KCC (South Korea), and RCM (Australia/New Zealand). For enterprises, these certifications are crucial to avoid deployment delays, financial penalties, and import restrictions. They also safeguard data centres and personnel from electrical hazards and interference. By having these global certifications, the H200 streamlines deployment and reduces the operational costs and risks associated with introducing new hardware into enterprise environments.

Universities are increasingly adopting Graphics Processing Units (GPUs) to accelerate research in fields like medicine, climate science, and artificial intelligence, which depend on processing massive datasets. Their parallel processing capabilities enable breakthroughs in complex tasks such as protein folding, large-scale climate modelling, and analysing cultural texts.

The NVIDIA H100 GPU is a key technology in this shift, offering significant improvements in speed, memory bandwidth, and energy efficiency, allowing researchers to undertake larger projects. Beyond research, GPUs are being integrated into university curricula to prepare students for the modern AI workforce.

While institutions face challenges like high costs and management complexity, recommendations include investing in shared clusters, forming vendor partnerships, and adopting hybrid on-premises and cloud models to maximise investment and foster innovation.

Networking has become a critical foundation for modern AI, high-performance computing, and cloud data centers. Training large language models, running simulations, or supporting real-time applications requires thousands of GPUs and CPUs working together. To make this possible, the infrastructure must move massive amounts of data quickly and reliably.