Network Optimization for AI: Best Practices and Strategies

The advent of artificial intelligence (AI) has ushered in a new era of data processing, demanding unprecedented levels of network optimization and performance. According to IDC Research, 47% of North American enterprises reported that generative AI (Gen AI) had a significantly larger impact on connectivity strategy and roadmaps in 2024. This is up from 25% in mid-2023. In addition, another 32% stated that Gen AI will have some impact on current connectivity transformation plans.¹

For IT networking professionals and leadership teams, figuring out how to bolster your network to support AI workloads is critical. AI and machine learning (ML) applications are bandwidth-intensive and require fast connectivity for real-time processing and insights. These workloads involve transferring massive datasets across the network, necessitating a robust infrastructure that can handle high volumes of data with minimal delay to support efficient data handling, scalability and real-time processing.

This guide provides some insights into the necessary bandwidth, latency and scalability requirements to prepare your network for the AI era, as well as some suggestions on how Lumen can help.

Critical Bandwidth Requirements for AI Network Optimization

Bandwidth is a critical factor in AI network optimization. As the maximum rate at which data can be transmitted over a network connection in a given amount of time, bandwidth determines how much data can flow through your network. AI workloads—especially those involving video analytics, natural language processing with its computational linguistics and complex simulations—require high bandwidth to move large datasets quickly. Networking professionals should assess current bandwidth usage and anticipate future needs while considering the exponential growth in data that AI applications will generate. Here are some key considerations for forecasting your bandwidth requirements for AI use and innovation:

1. Identify AI workloads: Determine the types of AI workloads you’ll be running. Machine learning and deep learning models, like email filtering and speech recognition, require substantial data processing and storage capabilities.
2. Estimate data volume: Calculate the volume of data your AI applications will generate and process. This includes training data (data used to train machine learning models), real-time data processing and any data transfers between systems.
3. Evaluate model complexity: More complex AI models, such as those using deep learning, will demand higher bandwidth for training and inference.
4. Plan for scalability: Ensure your infrastructure can scale to meet future demands. Fiber-optic networks, such as the Lumen Network, offer high-speed, high-bandwidth connectivity that can support growing AI needs. A newer connectivity option, Lumen Digital allows for on-demand scalability and agile bandwidth expansion with its cloud-like usage model, helping you to keep your data and applications running at peak performance.

Reducing Latency for Real-Time AI Applications

Low latency is crucial for AI applications that rely on real-time data analysis, such as autonomous vehicles, financial trading algorithms and instantaneous fraud detection systems. Latency refers to the delay between a user’s action and the response from the system. In the context of network performance, it is the time it takes for data to travel from the source to the destination and back again. Techniques to reduce latency include deploying edge computing strategies, where data processing occurs closer to the source of data generation and optimizing network protocols and configurations to streamline data flow.

Deploying a low-latency network with ultra-fast fiber connectivity helps to ensure that AI applications have the speed needed to process data in milliseconds. The Lumen Network is designed to help promote low latency with its extensive fiber footprint that allows for diverse network paths at both a metropolitan and national level.

Scalability: Preparing for AI Growth

AI applications can scale rapidly; thus, network infrastructure must have the ability to expand as data volumes increase. Virtualization and cloud-based services offer flexibility and scalability, allowing networks to adapt to varying AI workloads without the need for constant hardware upgrades. Implementing elastic cloud computing resources and virtual network functions can also provide the agility needed to scale with AI demands.

Modern network architectures, like Lumen^® Private Connectivity Fabric^SM (PCF), give organizations the power to create their own optical fiber networks. This setup is super flexible and modular, meaning services can be delivered seamlessly over the network. With an API software application, you can activate, manage and monitor services programmatically, to help ensure your network is used optimally and can scale as needed.

Many enterprises, data centers and even hyperscalers hit a bandwidth wall when their old network infrastructure can’t keep up with new technologies like AI, neural networks and robotics. PCF tackles these challenges by providing secure, fast connections that are ready for the AI revolution. Its modular approach lets customers build networks that are adaptable, scalable and redundant, tailored to their unique needs. PCF addresses issues related to bandwidth, latency and the rapid growth of AI workloads, helping to ensure a robust and efficient network infrastructure.

Future Trends in AI Networking

The future of networking in the AI era will likely see advancements in quantum networking, which promises to dramatically increase speed and security, and the wider adoption of edge computing to reduce latency. Quantum networking is an advanced form of networking that uses the principles of quantum mechanics to transmit information. Here are some key points to understand:

1. Quantum bits (qubits): Unlike classical bits, which are either 0 or 1, qubits can exist in multiple states simultaneously due to superposition. This allows for more complex and efficient data processing.
2. Entanglement: Quantum entanglement is a phenomenon where qubits become interconnected such that the state of one qubit instantly influences the state of another, regardless of distance. This property is crucial for quantum networking as it enables secure and instantaneous data transmission.
3. Quantum communication: Quantum networks facilitate the transmission of qubits between quantum processors. This can be used for applications like quantum key distribution, which provides highly secure communication channels.
4. Quantum internet: The ultimate goal of quantum networking is to create a quantum internet, where quantum information can be transmitted over long distances, linking quantum computers and sensors globally.

Quantum networking is still in its experimental stages, but it holds the potential to revolutionize data security and computational power. Staying abreast of these trends and technologies will be crucial for networking professionals looking to optimize their infrastructures for AI.

Conclusion

As AI continues to transform industries, the need for optimized network infrastructure has never been greater. By focusing on bandwidth, latency and scalability, networking professionals can optimize their infrastructure for AI, helping their companies to harness the full potential of AI. Implementing scalable, flexible and secure networking solutions will be key to supporting the demanding requirements of AI workloads now and in the future.

Most business owners see AI as a game-changer for their businesses, helping to improve customer relationships, boost productivity and drive sales growth. AI is valued for enhancing decision-making, speeding up response times and reducing errors. Businesses also expect AI to help cut costs and streamline processes.

Whether your organization is building for the future of AI, preparing for AI intensive applications or modernizing your network for today’s non-AI business requirements, a network and data strategy is required. Lumen can help you bring your network strategy to life by providing a high-performance network that scales to meet your needs today—and tomorrow—as you navigate complex data demands. And by following these best practices and staying informed about future trends, Lumen can help you optimize your network for better business results.

For more information on how Lumen Network can turbo charge your data and AI goals, visit the Lumen AI and Networking page.

¹IDC, Future Enterprise Resiliency & Spending Survey Wave 6, June 2024.

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