Which two actions should an AI infrastructure team prioritize to maintain consistent performance during peak usage hours?

The choice of using NVIDIA GPU Cloud (NGC) to automate updates and patches is an effective action for maintaining consistent performance during peak usage hours. Automating the management of updates and patches ensures that the AI infrastructure remains up to date with the latest optimizations, improvements, and security features provided by NVIDIA. This is particularly important during peak usage, as performance can significantly drop if the infrastructure is not operating with the most efficient and stable software versions. Automated processes also reduce the risk of human error, ensuring that updates are applied consistently and efficiently, thus minimizing downtime and disruptions.

Implementing dynamic resource allocation using NVIDIA GPU virtualization is another key action that complements the goal of maintaining performance. This approach allows the AI infrastructure team to allocate GPU resources based on real-time demand, ensuring that resources are available where they are needed the most. By allowing for flexibility and responsiveness to fluctuating workloads, the infrastructure can better handle the increased demand during peak usage times, thereby optimizing performance.

In contrast, manually monitoring GPU usage is less efficient and can lead to delays in addressing performance issues, as it relies on the individual checking logs rather than employing an automated system. Relying solely on CPU-based monitoring tools overlooks the specific needs and characteristics of GPU resources, which are crucial for