As AI large-model training clusters scale toward tens of thousands and even hundreds of thousands of GPUs, the bandwidth, power consumption, and latency pressures on interconnect networks continue to rise sharply. Traditional electrical packet switches (EPS) increasingly reveal limitations—high power draw, multi-hop latency, and escalating hardware costs—when handling high-bandwidth, long-lived collective communication. Optical Circuit Switches (OCS), with their unique advantages of rate transparency, pure optical pass-through, and reconfigurable topology, have emerged as a key technology direction for next-generation intelligent computing networks.

However, from the perspective of device principles and system capabilities, OCS cannot fully replace traditional electrical packet switches on its own. Hybrid networking of OCS and EPS, leveraging complementary strengths, remains the mainstream deployment paradigm in current data centers and AI computing clusters. A clear understanding of OCS application boundaries and networking logic is essential to avoid technical misconceptions and realize engineering value.

The core operating logic of OCS is to establish port-to-port physical optical paths in the optical domain for direct signal forwarding. The entire process involves no optical-electrical-optical (OEO) conversion and performs no packet parsing, buffering, or replication. This mechanism grants OCS significant advantages in protocol and rate transparency, high bandwidth, and low power consumption, while simultaneously creating inherent limitations: the device itself lacks dynamic multicast and packet-replication capabilities, making it poorly suited for native support of small-packet bursts or flexible multi-cast traffic. It is precisely this combination of strengths and weaknesses that necessitates collaborative division of labor between OCS and EPS, rather than simple technological substitution.

1. OCS Scenario Suitability Classification
Based on traffic characteristics and the predictability of communication patterns, business scenarios can be classified into high, medium, and low suitability levels for OCS. This classification not only guides technology selection but also directly shapes the pace of industrial deployment.

High Suitability (Priority Deployment Scenarios)
AI large-model training represents the most critical and commercially valuable scenario for OCS. During training, large volumes of collective communication traffic—such as AllReduce, AllGather, and All-to-All—typically account for more than 70% of total cluster traffic, and the communication patterns exhibit strong predictability across training iterations. OCS can establish dedicated optical paths for these collective operations, significantly reducing network hop counts, lowering queuing latency, and offloading bandwidth pressure from electrical switches. Google’s TPU clusters have already completed large-scale engineering validation, clearly demonstrating the practical value of OCS in high-bandwidth, long-lived connection scenarios. In clusters of ten thousand GPUs or more, this offloading effect becomes particularly pronounced, effectively alleviating congestion and power bottlenecks at the electrical switching layer.

High-throughput point-to-point transmission is another high-suitability scenario. For deterministic high-bandwidth workloads such as compute migration, dataset synchronization, and model parameter synchronization, traffic is continuous and highly predictable. OCS can construct low-jitter direct optical paths that fully exploit the bandwidth and power advantages of optical-domain transmission, thereby reducing overall network power consumption and hardware costs. These workloads are sensitive to latency jitter, and the pure optical pass-through characteristic of OCS matches the requirement precisely.

Dynamic topology reconfiguration further highlights the elastic value of OCS. AI training switches among data parallelism, tensor parallelism, pipeline parallelism, and other strategies according to task phases, causing internal communication patterns to change accordingly. OCS, guided by software-defined scheduling, can dynamically adjust optical-path topology to match the communication needs of each stage, enabling elastic allocation and efficient utilization of network resources. Compared with fixed-topology electrical networks, this dynamic reconfiguration capability markedly improves resource efficiency.

Medium Suitability (Requires Hybrid Networking)
General-purpose data-center workloads fall into the medium-suitability category. Ordinary cloud and internet traffic is heterogeneous, mixing large flows, small-packet access, and control signaling. In such environments, standalone OCS deployment is impractical. A typical approach is a hybrid architecture in which “EPS provides the foundational fabric while OCS offloads large flows”: electrical switches handle random bursts and small packets, while OCS carries the higher-proportion, long-duration large flows. This model preserves the flexibility of electrical switching while leveraging the optical layer to reduce power and cost on core links, making it a realistic evolution path for most data centers.

Low Suitability (Unsuitable for Independent OCS Carriage; Requires Electrical Switching Support)
Micro-burst traffic constitutes a clear weakness of OCS. Mainstream MEMS-based OCS devices typically require millisecond-scale reconfiguration times—far longer than the inter-arrival intervals of small-packet bursts. Faced with high-frequency random small packets, the optical path has not yet completed reconfiguration when the traffic has already ended, rendering the performance advantages of OCS unusable. Such traffic must be handled by electrical packet switches; otherwise, significant performance degradation occurs.

Multicast and broadcast traffic are similarly unsuitable for independent OCS carriage. OCS inherently lacks packet-replication capability and cannot efficiently implement one-to-many or many-to-many broadcast and multicast forwarding. Intra-cluster broadcast control messages, heartbeats, and similar traffic must rely on EPS for replication and distribution. Forcing OCS to handle these flows would be both inefficient and wasteful of valuable port resources.

2. Division of Labor in Hybrid Networking
In a photoelectric hybrid architecture, OCS and EPS form a clear division of capabilities that together constitute a complete network system. This division is not a temporary compromise but an inevitable outcome of their respective physical principles and capability boundaries.

OCS is positioned as the “optical backbone artery” of the computing network. It is responsible for carrying the highest-proportion, longest-duration, and most predictable large flows within the cluster, performing dynamic optical-path scheduling, reducing switching hierarchy, lowering investment in optical modules and switches, and fundamentally relieving the power and bandwidth walls of the computing network. By offloading the large flows that consume the most electrical-switching resources onto the optical layer, OCS significantly improves overall network efficiency—especially in clusters of ten thousand GPUs or larger.

EPS, in contrast, serves as the “capillary network and fallback foundation.” It handles small-packet bursts, multicast/broadcast, control signaling, management traffic, and other workloads that OCS handles poorly, thereby ensuring the network’s generality, flexibility, and reliability, and guaranteeing stable operation under complex traffic conditions. The fast forwarding and flexible replication capabilities of electrical switching remain indispensable complements within the hybrid network.

In practical deployments, a common hybrid architecture places OCS at the core or aggregation layer specifically for large-flow handling, while the edge and access layers remain primarily EPS-based. The scheduling system must sense traffic characteristics and dynamically decide which flows traverse optical paths and which traverse electrical paths, thereby achieving optimal resource allocation.

3. Implications for Industrial Deployment
The market frequently harbors the misconception that “OCS will completely replace electrical switches.” Engineering practice has repeatedly demonstrated that the true value of OCS lies not in handling every type of network traffic, but in offloading the large flows that most heavily consume electrical-switching resources onto the optical layer, thereby substantially improving overall network efficiency. This “offload rather than replace” positioning is the real industrial value of OCS.

From the perspective of industrial pacing:
· High-suitability scenarios—AI training and high-throughput point-to-point traffic—will be the first to achieve large-scale commercial deployment of OCS;
· General-purpose data-center scenarios will serve only as secondary expansion directions;
· Small-packet bursts and multicast/broadcast traffic will continue to be dominated by electrical switching for the foreseeable future.

Technology deployment must proceed step by step and cannot be achieved overnight.

Looking ahead, competition in the OCS industry will no longer be limited to the performance of optical-switch devices themselves. It will increasingly test complete system capabilities, including optical-layer scheduling software that deeply integrates with AI job schedulers, fully automated optical-path calibration and operations systems, and hybrid networking solutions that coordinate with electrical switching equipment. Only when devices, software, and networking schemes mature in concert can OCS fully release its technical value and truly become the core interconnect foundation of AI intelligent computing clusters.

In an era of continuously exploding AI computing power, OCS is not a “replacement” for electrical switches, but an indispensable “optical backbone artery” within the network architecture. Only by correctly recognizing its application boundaries can the technology move from the laboratory to large-scale deployment and genuinely support the construction of next-generation ultra-large-scale intelligent computing infrastructure. Only by performing hybrid networking within clearly defined boundaries can light and electricity each play to their strengths, jointly building efficient, low-power, and scalable intelligent computing networks.