As artificial intelligence (AI), large-scale AI models, and high-performance computing (HPC) continue to advance, data centers are entering an era of unprecedented interconnectivity. The rapid growth of GPUs, TPUs, and other accelerators is driving exponential increases in east-west traffic within AI clusters, placing growing pressure on conventional electrical switching architectures in terms of bandwidth, power consumption, latency, and scalability.

Against this backdrop, Optical Circuit Switching (OCS) is evolving from an emerging networking technology into an increasingly important infrastructure solution for next-generation AI data centers.

But where is the market demand for OCS actually coming from?

1. The First Driver: The Rapid Expansion of AI Compute Clusters
The most direct driver of the OCS market is the rapid expansion of AI computing infrastructure. Training large AI models requires thousands or even tens of thousands of GPUs or TPUs to operate collaboratively. As the number of compute nodes increases, the volume of communication between these nodes grows accordingly. Networking is no longer simply a connectivity layer—it has become a critical factor determining the overall efficiency of AI clusters.

Large-scale GPU and TPU clusters require high-throughput, low-latency communication to efficiently exchange massive amounts of data. While conventional electrical switching networks remain highly mature, continuously increasing port speeds are also resulting in greater power consumption and architectural complexity.

Optical switching offers a different approach:
Keep data in the optical domain for as much of the transmission and switching process as possible. By establishing direct optical paths between endpoints, OCS can reduce unnecessary optical-electrical-optical (O-E-O) conversions, helping lower network power consumption while providing high-bandwidth and protocol-transparent connectivity. As AI workloads continue to scale, optical circuit switching is therefore gaining attention as a potential solution for building more efficient and scalable AI network architectures.

2. The Second Driver: 800G and 1.6T Are Accelerating Network Upgrades
The growth of AI clusters is not only increasing the number of network ports—it is also driving a rapid increase in per-port bandwidth.800G has emerged as an important connectivity standard for next-generation AI data centers, while 1.6T is becoming a key direction for future network evolution. As network speeds progress from 400G to 800G and eventually to 1.6T and beyond, conventional electrical switching infrastructure faces increasing challenges in terms of bandwidth density, power consumption, thermal management, and system complexity.

This raises an important question:
Does every data exchange in an AI network still need to go through complex electronic processing?
Not necessarily.

For relatively stable and sustained high-bandwidth traffic flows, OCS can establish optical connections directly in the optical domain, allowing data to bypass certain stages of electronic switching and processing. As a result, the rapid deployment of high-speed optical interconnects is creating a new market opportunity for optical switching technologies.

3. The Third Driver: Growing Pressure to Reduce Data Center Power Consumption

The expansion of AI computing comes with another major challenge: energy consumption.GPUs and other AI accelerators already require significant amounts of power. At the same time, network infrastructure represents an increasingly important component of overall data center energy consumption. As AI clusters grow, improving the energy efficiency of computing alone is no longer sufficient. Networking infrastructure must also become more energy efficient. This is one of the key reasons why OCS is attracting increasing attention. Unlike conventional electrical switching equipment, OCS primarily performs switching in the optical domain without electronically processing every bit of data. This enables several potential advantages, including:
· Lower power consumption
· Lower latency
· High bandwidth transparency
· Reduced O-E-O conversion
· Efficient large-scale optical connectivity

These characteristics make optical switching particularly attractive for large-scale AI and HPC environments, where network traffic and bandwidth requirements continue to grow rapidly.

The broader trend is clear:
The competition in AI infrastructure is evolving from computing performance alone toward a combination of compute capacity, network performance, and energy efficiency. OCS sits at the intersection of all three.

4. The Fourth Driver: AI Networks Require More Flexible Connectivity
Another important difference between AI data centers and traditional data centers is the highly dynamic nature of AI workloads.During AI model training, different GPUs, TPUs, servers, and storage resources may generate sustained high-bandwidth communication requirements. Once a workload is completed, however, the required communication patterns may change significantly.

Future AI networks therefore need more than simply higher bandwidth. They also require:
· Dynamic resource allocation
· Flexible optical path reconfiguration
· High port density
· Low-latency connectivity
· High-bandwidth transparent transmission
· Network optimization based on workload characteristics

OCS provides a physical-layer mechanism for dynamic optical path reconfiguration. Using MEMS and other optical switching technologies, network operators can dynamically establish and reconfigure optical paths between different computing resources according to network requirements. This means OCS is no longer simply an optical switching component. It has the potential to become an important infrastructure layer for AI network resource management and traffic optimization.

5. The Fifth Driver: The Evolution from Scale-Out to More Complex Scale-Up Architectures
Historically, AI clusters have largely followed a Scale-Out approach—adding more compute nodes and connecting them through increasingly capable networks.Today, AI infrastructure is also moving toward larger-scale Scale-Up architectures, in which compute resources need to be connected more tightly and efficiently. This creates new requirements for high-bandwidth, low-latency connections between GPUs, accelerators, memory, storage, and other computing resources. As AI architectures become increasingly interconnected, the role of optical switching is expected to expand beyond conventional data center connectivity. OCS can potentially support both Scale-Out and Scale-Up architectures by providing flexible, high-bandwidth optical connectivity between large numbers of compute resources. The result is a broader potential market for OCS across next-generation AI computing infrastructure.

6. The Market Demand for OCS Comes Down to Three Fundamental Changes
Looking at the market from a broader perspective, demand for OCS is not emerging in isolation. It is being driven by three fundamental changes in data center infrastructure.

1. Computing clusters are becoming larger
More GPUs, TPUs, and other accelerators mean more compute nodes, more complex communication patterns, and significantly greater network traffic.

2. Network speeds are becoming higher
The evolution from 400G to 800G, 1.6T, and beyond is increasing pressure on conventional switching architectures.

3. Energy efficiency is becoming a strategic priority
As AI infrastructure expands, reducing the energy consumption of every compute node, network link, and switching device becomes increasingly important to the overall economics of data center operations.

These three factors form the fundamental market logic behind OCS:
Growing compute capacity drives traffic growth. High-speed optical interconnects drive network upgrades. Increasing energy pressure accelerates the adoption of optical switching technologies.Together, they are creating a strong foundation for the growth of the OCS market.

7. From an Emerging Technology to an Infrastructure Component
In the past, OCS was often regarded primarily as a promising optical networking technology.

Today, that perception is changing. Major technology companies and hyperscale data center operators are actively exploring optical circuit switching for AI infrastructure, while the broader ecosystem—including MEMS, silicon photonics, high-speed optical transceivers, and optical switching systems—is continuing to mature.

This represents an important transition:
· OCS is moving from technology-driven development toward demand-driven deployment.
· The market does not simply need another optical switching device. It needs a networking architecture capable of delivering:
· Higher bandwidth, lower power consumption, lower latency, greater connectivity flexibility, and improved long-term scalability.
· OCS has the potential to address these requirements at the optical layer.

8. GLSUN: Enabling Next-Generation AI Networks with Optical Switching
As AI computing infrastructure continues to scale, GLSUN is advancing its portfolio of MEMS optical switching and Optical Circuit Switching (OCS) solutions for data centers, AI networks, Data Center Interconnect (DCI), HPC, and optical network management applications. GLSUN's OCS solutions are based on advanced MEMS optical switching technology and support scalable optical matrix configurations, including 32×32, 64×64, and larger port configurations, providing flexible optical connectivity for different data center and AI cluster architectures. By establishing optical paths directly in the optical domain, OCS can reduce unnecessary optical-electrical conversions and provide a more efficient architecture for high-speed, low-latency, and energy-conscious networks. As AI clusters continue to scale, networking will become an increasingly critical factor in determining overall computing efficiency.

The market opportunity for OCS is therefore not being created by optical switching alone. It is the result of three powerful forces converging:
the rapid growth of AI compute, the evolution toward higher-speed optical interconnects, and the increasing demand for energy-efficient data center infrastructure.

As AI clusters move toward larger scales and 800G, 1.6T, and future higher-speed interfaces become increasingly common, optical switching is positioned to evolve from an innovative networking technology into a key infrastructure component for next-generation AI and HPC networks.

GLSUN — Connecting the Next Generation of AI Computing with Optical Switching.