AI networks are evolving at an unprecedented pace, mainly due to the exponential growth of AI capabilities and the surge in demand for computing power. To meet these demands, operators are adopting the following four core strategies to build networks with powerful performance, scalability, and innovation to support next-generation AI applications:

Interconnected AI data centers for smarter operations

As ultra large scale data centers face multiple limitations in terms of power, land availability, and internal physical space, operators are shifting towards the “remote AI data center” model. This trend includes dispersing AI workloads across multiple interconnected campuses and building fiber optic networks that can connect multiple data center locations over long distances.

For Large Language Models (LLMs) and other AI systems, distributing computing, memory, and power across different campuses can improve performance and efficiency. This distributed approach requires low latency, high bandwidth fiber optic cabling to support the stringent requirements of AI for intensive data processing. Long distance fiber optic networks are gradually becoming the backbone of distributed AI infrastructure, enabling networks to pre train and run large-scale AI models between multiple data centers while maintaining seamless connectivity.

Layout in advance for future scalability needs, rather than remedying them afterwards

The most successful ultra large scale networks are able to anticipate future needs in advance, rather than waiting until problems arise to make adjustments. Scalability has become a decisive factor in data center construction, especially in the context of increasingly complex AI terminal applications and workloads. Modern AI models require larger scale, high bandwidth GPU clusters that are interconnected through large-scale fiber optic networks to cope with massive AI computing workloads. These clusters are no longer limited to a single server or rack (i.e. “scale up”), but are expanding to multiple racks, buildings, and even parks – a growth trend known as “scale out”.

This evolution requires larger switches, multi planar network architecture, and high-density cabling architecture to enable GPU interconnection within scalable units. As AI nodes expand to larger networks, the demand for cabling also doubles. The demand for fiber optic in generative AI networks is 10 times that of traditional data centers. In the past, this type of architecture typically used copper cables, but with the growing demand for higher bandwidth and longer distance connections (up to 100Gbps per meter), fiber optic has become a more economical and space saving choice.

Accelerating network speed through Co Packaged Optics (CPO) technology

Co Packaged Optics (CPO) technology represents a transformative innovation in the field of network design, integrating optical and electronic devices in the same package to improve processing speed and energy efficiency. By directly integrating optical components into the switch, CPO eliminates the step of long-distance transmission of electrical signals before converting them into optical signals, effectively reducing latency and improving performance.

The adoption of CPO technology enables operators to build large switches with higher port density and better efficiency, breaking through the limitations of traditional pluggable optical modules. This transformation is crucial for addressing the enormous bandwidth demands brought about by AI workloads, while also reducing total cost of ownership (TCO) and enhancing scalability. As the new generation of servers is designed around CPO, this technology is expected to play a central role in driving ultra large scale networks to keep up with the rapid development of AI.

Innovative fiber optic solutions empower the future development of AI

The explosive growth of AI is driving network operators to innovate boldly to support the evolution of network infrastructure. From horizontally expanding GPU clusters, to building distributed campuses interconnected by long-distance fiber optics, to adopting CPO technology, innovation capability has become the key to maintaining leadership in the AI era.

Corning’s GlassWorks ™ The innovative products in the AI solution portfolio are at the core of these technological advancements, providing the required bandwidth, low latency, and high energy efficiency for AI networks. By investing in cutting-edge fiber optic and connectivity technologies, operators can build AI networks capable of supporting increasingly complex model training and inference, while reducing operating costs.

The future path of AI networks is clear: operators must focus on infrastructure scalability, distributed deployment, and technological innovation to support the development of next-generation AI applications. By embracing these four key trends – scalability, distributed networks, CPO technology, and advanced fiber optic solutions – operators can “build tomorrow’s networks today” and fully unleash the full potential of AI.