Introduction to AI-Ready Networking
To manage IT complexity, organizations are turning to technology partners to create central hubs for their operations. A recent example of this is the Ryder Cup, which engaged HPE to create a platform for tournament staff to access data visualization and support operational decision-making. This dashboard leveraged a high-performance network and private-cloud environment, aggregating and distilling insights from diverse real-time data feeds.
The Importance of Networking in AI Implementation
It was a glimpse into what AI-ready networking looks like at scale—a real-world stress test with implications for everything from event management to enterprise operations. While models and data readiness get the lion’s share of boardroom attention and media hype, networking is a critical third leg of successful AI implementation, explains Jon Green, CTO of HPE Networking. “Disconnected AI doesn’t get you very much; you need a way to get data into it and out of it for both training and inference,” he says.
The Future of Networking
As businesses move toward distributed, real-time AI applications, tomorrow’s networks will need to parse even more massive volumes of information at ever more lightning-fast speeds. What played out on the greens at Bethpage Black represents a lesson being learned across industries: Inference-ready networks are a make-or-break factor for turning AI’s promise into real-world performance.
Making a Network AI Inference-Ready
More than half of organizations are still struggling to operationalize their data pipelines. In a recent HPE cross-industry survey of 1,775 IT leaders, 45% said they could run real-time data pushes and pulls for innovation. It’s a noticeable change over last year’s numbers (just 7% reported having such capabilities in 2024), but there’s still work to be done to connect data collection with real-time decision-making.
The Role of Infrastructure Design
The network may hold the key to further narrowing that gap. Part of the solution will likely come down to infrastructure design. While traditional enterprise networks are engineered to handle the predictable flow of business applications—email, browsers, file sharing, etc.—they’re not designed to field the dynamic, high-volume data movement required by AI workloads. Inferencing in particular depends on shuttling vast datasets between multiple GPUs with supercomputer-like precision.
Characteristics of AI-Ready Networks
Networks built for AI, therefore, must operate with a different set of performance characteristics, including ultra-low latency, lossless throughput, specialized equipment, and adaptability at scale. One of these differences is AI’s distributed nature, which affects the seamless flow of data.
Real-World Example: The Ryder Cup
The Ryder Cup was a vivid demonstration of this new class of networking in action. During the event, a Connected Intelligence Center was put in place to ingest data from ticket scans, weather reports, GPS-tracked golf carts, concession and merchandise sales, spectator and consumer queues, and network performance. Additionally, 67 AI-enabled cameras were positioned throughout the course. Inputs were analyzed through an operational intelligence dashboard and provided staff with an instantaneous view of activity across the grounds.
Conclusion
In conclusion, AI-ready networking is crucial for organizations to turn AI’s promise into real-world performance. With the increasing demand for distributed, real-time AI applications, networks must be designed to handle the dynamic, high-volume data movement required by AI workloads. By understanding the importance of networking in AI implementation and designing infrastructure with AI-ready performance characteristics, organizations can unlock the full potential of AI and stay ahead in the game.
FAQs
Q: What is AI-ready networking?
A: AI-ready networking refers to the design and implementation of networks that can handle the high-volume, dynamic data movement required by AI workloads.
Q: Why is networking important in AI implementation?
A: Networking is a critical third leg of successful AI implementation, as it enables the flow of data into and out of AI systems for both training and inference.
Q: What are the characteristics of AI-ready networks?
A: AI-ready networks must operate with ultra-low latency, lossless throughput, specialized equipment, and adaptability at scale.
Q: What is the role of infrastructure design in making a network AI inference-ready?
A: Infrastructure design plays a crucial role in making a network AI inference-ready, as traditional enterprise networks are not designed to handle the dynamic, high-volume data movement required by AI workloads.
