Webinar Recap: Dynamically Orchestrating RAN and AI Workloads on a Common GPU Cloud

In the recent webinar, "Dynamically Orchestrating RAN and AI Workloads on a Common GPU Cloud," the presenters highlighted innovative strategies for optimizing GPU infrastructure. The focus was on leveraging dynamic orchestration to manage both RAN and AI workloads efficiently, maximizing resource utilization and ROI for Mobile Network Operators (MNOs).

Key Takeaways:

  1. RAN and AI workloads on the same GPU cloud:some text
    • 5G RAN L1 layer acceleration is achieved by using the GPUs for processing. The same GPU infrastructure could also be used for running the AI workloads. Thus by configuring the same GPU infrastructure for both types of workloads, MNOs can significantly improve utilization rates.
  2. Dynamic Scaling:some text
    • The webinar demonstrated how dynamic scaling techniques allow RAN and AI workloads to scale in and out based on real-time traffic demands. This automation ensures optimal use of resources, reducing operational costs and enhancing performance.
  3. Monetizing Unused Capacity:some text
    • Traditional RAN infrastructure is provisioned for peak hours and hence is often underutilized during off-peak periods. This causes revenue loss especially because of costly GPU computes. MNOs can capitalize on their infrastructure by selling unused GPU cycles as spot instances for running the AI workloads. This additional revenue stream can significantly shorten the ROI period for existing investments.
  4. Automation and Efficiency:some text
    • Automating the orchestration of RAN and AI workloads minimizes manual intervention, leading to greater efficiency and consistency. This approach also simplifies management and operational tasks, allowing MNOs to focus on strategic initiatives.

Demo Highlights:

The webinar included a live demonstration of dynamic orchestration in action, showcasing real-world applications and benefits. Attendees were able to see firsthand how automated scaling and resource management can transform infrastructure utilization.

Conclusion:

The integration of RAN and AI workloads on a common GPU cloud represents a significant advancement for MNOs, offering enhanced efficiency, reduced costs, and new revenue opportunities. As the telecom industry continues to evolve, adopting such innovative solutions will be crucial for staying competitive and maximizing infrastructure investments.

For those who missed the live session, you can watch the recorded webinar here.

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