Optical Interconnects for AI Data Centers: From Pluggable Optical Modules to Co-Packaged Optics

Optical interconnects for AI data centers are high-speed data links that use light to move information between GPUs, switches, racks, and data-center systems. They matter because large AI clusters need more than raw compute power: they also need high-bandwidth, low-latency, power-efficient data movement across many devices.

For the last few years, most AI infrastructure discussions have centered on GPUs. That focus is understandable, because GPUs provide the parallel compute needed for large-scale training and inference. But a GPU cluster is not just a pile of accelerators. It is a distributed computing system, and distributed systems are limited not only by how fast each processor can calculate, but also by how fast data can move between processors.

When thousands of GPUs work together, the interconnect becomes part of the compute system itself. If the data path between GPUs, switches, and racks cannot keep up, expensive accelerators spend more time waiting and less time computing. In that sense, optical interconnect is not a peripheral networking topic. It is one of the physical layers that determines whether large AI systems can use their installed compute effectively.

AI training is the easiest place to see the problem. A large model may contain enormous numbers of parameters, far beyond what a single GPU can hold or process efficiently. The workload is divided across many accelerators. Each GPU computes part of the task, then exchanges intermediate results with other GPUs. That exchange can happen repeatedly during training, creating heavy east-west traffic inside the AI cluster.

Inference also used to look simpler. In an earlier generation of AI applications, it was reasonable to imagine a query being handled by a small number of GPUs. Modern inference is moving toward more complex reasoning, longer context, retrieval, tool use, planning, and agentic workflows. In these cases, the system may need to coordinate more compute resources across more steps. The result is that inference can also become an interconnect-sensitive workload, especially when the deployment serves many users at scale.

The practical lesson is straightforward: once AI workloads require many processors to act as one system, GPU interconnect bandwidth becomes part of the performance equation.

Training and inference place different pressure on the network, but both depend on data movement.

During training, GPUs exchange gradients, activations, parameters, and intermediate data. The more distributed the model and the larger the cluster, the more critical synchronization and data exchange become. During inference, the pressure depends on workload design. Simple request-response inference may not stress the network as much as training, but multi-step reasoning, retrieval, and agentic execution can increase communication between compute nodes, storage systems, and accelerator groups.

This is why optical interconnects have become central to AI data-center architecture. The challenge is no longer only how to build faster chips. It is also how to connect those chips in a way that keeps bandwidth high, distance manageable, latency low, and power consumption under control.

Copper still has an important place in AI systems. For very short electrical paths inside a server, chassis, or tightly integrated cabinet, copper can be efficient, serviceable, and cost-effective. The problem appears when the same copper-based approach is pushed toward higher lane rates, longer links, and larger cluster topologies.

At high speed, copper links face three connected constraints: signal integrity, reach, and power. The higher the data rate, the harder it becomes to send clean electrical signals over distance. Passive copper is typically limited to short links. Active copper solutions can extend reach by adding electronics, but those electronics add power, heat, cost, and design complexity.

SerDes technology has enabled very high-speed electrical interfaces, but higher signaling rates make copper links increasingly sensitive to loss, reflection, crosstalk, and equalization complexity. As AI systems move toward faster electrical lanes, the effective reach of copper becomes more product-dependent and architecture-dependent.

This does not mean copper disappears. It means copper is increasingly used where its strengths still match the physical distance: short, tightly controlled electrical paths. Once the link moves beyond a few meters, or once many links must operate densely in a rack-scale or cluster-scale system, optical links become more attractive.

The most important distinction is not “copper versus fiber” in the abstract. The real distinction is link distance and system layer.

Inside a cabinet, GPUs and switch chips may communicate over very short electrical paths. In systems such as high-density GPU cabinets, many internal links can remain electrical because the physical distance is short. But rack-to-rack, cabinet-to-cabinet, and data-center-scale links create a different problem. Those distances are longer, the link count is higher, and the cost of signal loss becomes much more visible at the system level.

Copper can still be engineered for specific short-reach applications. Fiber becomes compelling when the architecture requires high bandwidth across longer or more distributed connections.

Interconnect power is not just a line item in a component specification. At AI data-center scale, thousands or millions of high-speed lanes can turn link power into a major design constraint. Active copper links, retimers, equalization, and thermal management all add pressure to the system.

The final engineering question is not only whether a link can work. It is whether that link can work at scale, within the power and thermal envelope of a dense AI facility. This is one reason optical interconnects have moved from a networking topic into an AI infrastructure topic.

                                              Copper vs Fiber Interconnect in AI Data Centers

Fiber optic links use light rather than electrical current to carry information. That gives them several advantages in AI data centers: high bandwidth, long reach, immunity to electromagnetic interference, and better suitability for dense high-speed links over distance.

The value of fiber is especially clear when the system must connect multiple racks, multiple cabinets, or multiple data halls. Electrical copper signals degrade with distance and speed. Optical signals can travel much farther while maintaining high data rates, making fiber a natural fit for distributed AI clusters.

WDM, or wavelength division multiplexing, allows multiple optical wavelengths to travel through the same fiber at the same time. Each wavelength can carry a separate data stream. In practical terms, WDM turns one fiber into multiple parallel optical channels.

This is one reason optical links scale differently from copper links. Instead of adding a separate physical conductor for every traffic path, optical systems can increase capacity by combining wavelength channels, higher modulation formats, and faster optical components.

                                         WDM Multi-Wavelength Transmission in a Single Fiber

The right engineering choice depends on distance, bandwidth, cost, serviceability, and thermal design. Copper remains useful in short controlled links. Fiber becomes increasingly important as AI clusters scale outward.

A pluggable optical transceiver is a module that converts electrical signals into optical signals and optical signals back into electrical signals. One side connects electrically to a switch, network interface, or system board. The other side connects to optical fiber.

In AI data centers, pluggable optical modules are especially important for links between cabinets, racks, and switches. They are not usually the main technology for every short link inside a GPU cabinet. That distinction matters because it prevents a common misunderstanding: optical modules do not automatically replace all internal GPU wiring.

Inside a high-density GPU cabinet, the distance between GPUs, switches, and boards may be only centimeters to a small number of meters. Electrical links can still make sense there, especially where the system is designed as a tightly integrated unit.

When traffic leaves the cabinet and moves to another rack, another switch, or another room, the link requirements change. The distance becomes longer, the number of links grows, and optical modules become more attractive.

A useful way to think about the hierarchy is:

The demand chain is simple. More GPUs require more systems. More systems require more cabinets. More cabinets require more high-speed interconnect between cabinets and switches. As the number of these links increases, demand for optical modules increases.

This is why optical transceivers have become closely tied to AI infrastructure growth. The module is not valuable because it is a standalone box. It is valuable because it enables the physical network that lets large GPU clusters operate as one system.

A pluggable optical transceiver looks simple from the outside, but internally it combines optics, electronics, semiconductors, packaging, and precision alignment. The main components are the laser, modulator, photodetector, DSP, and optical coupling system.

                                                     Inside a Pluggable Optical Transceiver

The laser diode provides the light source for the optical signal. It does not necessarily carry the data by itself. Instead, it produces a stable optical carrier that can be modulated.

The material system matters. Silicon is excellent for digital logic, but it is not an efficient light emitter. Optical lasers commonly use III-V compound semiconductors such as InP or GaAs, because these materials are much better suited to generating light.

Several laser types appear in optical modules and related systems:

The shift from traditional pluggable optics toward silicon photonics and CPO changes the role of the laser. In many pluggable modules, the laser and modulator can be closely integrated. In CPO-style designs, the laser may sit outside the package as an external light source, while modulation happens inside the silicon photonics chip.

The modulator is the component that turns a blank optical carrier into a data-carrying signal. It takes the electrical data stream and changes the optical signal so that ones and zeros can be represented by light intensity or phase behavior.

Two important modulation approaches are EAM and MZI.

An electro-absorption modulator changes how strongly a material absorbs light when voltage is applied. It can be integrated with a laser to form an EML, which is widely used in conventional high-speed optical modules.

A Mach-Zehnder interferometer modulator works differently. It splits light into two paths, changes the phase in one path, and then recombines the light. Depending on the phase relationship, the recombined signal can become stronger or weaker. This approach is important in silicon photonics because it can be implemented using silicon waveguide structures.

At the receiving end, the optical signal must be converted back into an electrical signal. That is the role of the photodetector.

The photodetector uses the photoelectric effect: incoming photons excite carriers in the semiconductor material, creating current. A good photodetector must respond quickly, generate enough current from weak optical power, and keep noise low.

Three parameters matter especially:

In silicon photonics, germanium is often used for photodetection because silicon itself is not effective for absorbing common telecom wavelengths such as 1310 nm and 1550 nm. This is one example of how silicon photonics still depends on careful material integration, not pure silicon alone.

The DSP is the digital signal-processing engine inside many high-speed optical modules. It helps encode, equalize, recover, and clean up the signal.

At high speeds, the optical link is not just sending simple on-off pulses. Modern modules often use PAM4, which represents two bits per symbol using four signal levels. PAM4 improves bandwidth efficiency, but it also makes the signal more sensitive to noise and distortion. The DSP helps recover the intended data from that imperfect signal.

The optical module speed roadmap has moved from 400G to 800G, with 1.6T deployment and higher-rate designs pushing the industry toward faster electrical and optical lanes. The exact architecture depends on the module design, lane count, modulation scheme, and system requirement, but the trend is clear: every generation places more pressure on the DSP, optics, packaging, and test process.

The last critical function is optical coupling. Light generated or processed on a chip must enter the fiber with very high precision. A single-mode fiber core is only about 8–9 micrometers wide, so coupling is a micron-scale alignment problem.

Two common approaches are butt coupling and grating coupling.

Butt coupling sends light directly from the chip edge into the fiber. It can be efficient, but alignment is demanding. Grating coupling uses a patterned structure on the chip surface to redirect light into or out of a waveguide. It can provide more alignment tolerance in some designs, but it also introduces wavelength and efficiency considerations.

At production scale, the challenge is not simply demonstrating optical coupling once. The challenge is doing it repeatedly, reliably, and economically across large volumes.

An optical module can be understood as a two-way translation system. On transmit, it converts electrical data into optical data. On receive, it converts optical data back into electrical data.

                                                  Electrical-Optical-Electrical Signal Flow

In the transmit direction, the GPU or switch ASIC sends an electrical signal toward the optical module. The DSP conditions the signal. The modulator imposes the information onto light from the laser source. Coupling optics then align that light into the fiber.

In the receive direction, light exits the fiber and is directed onto the photodetector. The photodetector converts the optical signal into current. The DSP then recovers the data, corrects distortion, and sends a usable electrical signal back to the system.

This electrical-optical-electrical conversion is the foundation of pluggable optical interconnects.

Optical modules combine two semiconductor worlds that do not naturally merge.

The first is the silicon digital world. DSPs are silicon-based ICs. They rely on advanced CMOS design, digital signal processing, and high-speed electrical interfaces.

The second is the compound-semiconductor optical world. Lasers, many modulators, and some photodetectors rely on materials such as InP and GaAs. These materials are used because they can generate, modulate, or detect light efficiently in ways silicon cannot.

                                             Silicon DSP vs InP Optical Chip Manufacturing

A DSP is fundamentally a digital chip. It deals with symbols, coding, correction, equalization, and signal recovery. Its barriers are algorithmic complexity, high-speed mixed-signal design, and advanced silicon implementation.

This is closer to the world of CPUs, GPUs, switches, and networking ASICs than to the world of laser manufacturing. The design teams, process flows, and manufacturing partners are therefore different from those used for compound-semiconductor optical devices.

InP and GaAs optical devices belong to a different process ecosystem. The wafers are smaller, the materials behave differently, the process chemistry is different, and optical performance depends heavily on epitaxy, defect control, and device structure.

A leading silicon foundry is not automatically a leading InP laser manufacturer. The equipment, recipes, materials knowledge, and yield challenges are different. This is one reason optical interconnect supply chains are more distributed than GPU supply chains.

The substrate is the base material on which the optical device is built. For InP-based lasers, material quality is critical because defects can affect the optical device grown above it.

Epitaxy is the process of growing functional layers on the substrate. In laser devices, these layers can include quantum-well structures, where electrons and holes recombine to emit photons. Layer thickness, composition, and doping must be tightly controlled. Small deviations can shift wavelength, reduce efficiency, or hurt reliability.

This is why compound-semiconductor manufacturing is not simply “chip manufacturing with a different material.” It is a specialized optical-device manufacturing discipline.

Silicon photonics PIC technology uses silicon-based structures to guide, modulate, split, combine, and detect light on an integrated chip. It is important because it brings optical functions closer to the manufacturing and packaging world of advanced electronics.

A silicon photonics PIC does not mean every optical function is made from silicon alone. Silicon can guide light and support compact waveguides, modulators, and integration schemes. But silicon is not an efficient light source, so external or separately integrated III-V lasers remain important.

Silicon photonics often uses SOI, or silicon-on-insulator, as a platform. In simplified terms, SOI provides a silicon layer separated from the substrate by an insulating oxide layer. The high refractive-index contrast between silicon and silicon dioxide helps confine light inside compact silicon waveguides.

These waveguides act like optical wires on the chip. They route light between modulators, splitters, couplers, detectors, and other optical structures.

The key limitation is light generation. Silicon is useful for manipulating light, but it is inefficient as a laser material. That is why silicon photonics systems often rely on InP-based laser sources.

This division of labor is central to CPO architecture. The silicon photonics PIC can sit close to the ASIC and handle waveguiding, modulation, and detection. The laser can remain outside the package as an external light source, feeding continuous light into the photonic chip.

Co-packaged optics, or CPO, moves optical functions closer to the switch ASIC, GPU-adjacent compute architecture, or package-level electronics. Instead of placing every optical conversion function in a pluggable module at the back of a system, CPO integrates optical engines much closer to the chip.

NVIDIA describes its CPO switch approach as replacing pluggable transceivers with silicon photonics on the same package as the ASIC. Broadcom similarly describes its CPO Ethernet switch architecture as integrating optical engines onto a common package with the switch. The engineering purpose is to shorten electrical distance, reduce the burden of high-speed electrical signaling, and improve power efficiency at high bandwidth density.

A simplified CPO architecture includes four main blocks:

                              CPO Architecture with Silicon Photonics PIC and External Laser Source

The architectural shift is the location of the optical interface. In a pluggable module, electrical signals travel from the chip or board to the module. In CPO, the optical interface moves closer to the ASIC package. That shorter electrical path is the main reason CPO is attractive for very high-density AI networking.

CPO does not eliminate lasers. It changes where they sit and what they do.

External laser sources can provide continuous light to the silicon photonics engine while remaining outside the hottest and most complex part of the package. This helps with serviceability and thermal design. If the laser is kept outside the package, it can be treated as a replaceable optical power source rather than an inseparable part of the ASIC package.

The laser source is still commonly based on III-V materials such as InP. Silicon photonics can bring optical routing and modulation close to the ASIC, but it still needs a proper light source.

CPO should not be understood as a universal replacement for pluggable optics. The two architectures serve different layers of the data-center network.

The more realistic future is coexistence. Pluggable optics will remain important across many data-center links. CPO will grow where bandwidth density and electrical power pressure are most severe.

The strongest engineering driver for CPO is not that it is “new.” It is that high-speed electrical distance becomes increasingly expensive as bandwidth density rises. Moving optical conversion closer to the ASIC reduces the length of the most difficult electrical path.

This can reduce the need for complex electrical retiming, improve signal integrity, lower link power, and support denser switch systems. However, CPO also increases the importance of optical packaging, laser source strategy, thermal design, and test complexity.

A pluggable optical architecture keeps the module physically separate from the ASIC. The electrical signal must travel across the board to reach the module. At very high speeds, that distance requires careful channel design and often active signal conditioning.

CPO changes this balance. By placing optical engines near the ASIC, it reduces the electrical distance before conversion to light. The optical path then carries the signal over fiber, where distance scaling is more favorable.

Vendor-reported CPO performance figures are product-specific and should be interpreted within the context of each switch architecture. NVIDIA’s public CPO materials describe improved network resiliency and sustained application runtime compared with pluggable-transceiver-based designs. Broadcom states that its Tomahawk 6 Davisson CPO Ethernet switch provides 102.4 Tbps of switching capacity and reduces optical interconnect power consumption by 70% compared with traditional pluggable solutions.

These claims are important signals, but they should not be generalized into “all CPO systems always deliver the same benefit.” The real benefit depends on switch architecture, optical engine design, link topology, thermal design, and deployment environment.

Optical interconnects depend on a chain of specialized technologies. A shortage or yield problem in one layer can limit the availability of the final module or system.

The supply chain can be understood in layers:

Compound-semiconductor substrates are the starting point for many optical devices. InP and GaAs are used because their material properties support light generation and detection in ways silicon cannot.

High-quality substrates are essential because defects can propagate into device layers and reduce performance or reliability. For AI data-center optics, this matters because high-speed modules and CPO light sources require stable, repeatable optical performance.

SOI wafers are important for silicon photonics because they provide the platform for compact optical waveguides and integrated photonic structures. They are not the only factor in silicon photonics, but they are a foundational input.

The importance of SOI increases as silicon photonics moves from specialized optical devices into high-volume data-center interconnect architectures.

The digital IC layer remains essential. Even as CPO reduces the role of long electrical paths, optical systems still need driver ICs, control logic, and signal-processing intelligence. In pluggable modules, the DSP can be one of the most complex and expensive components. In CPO, some signal-processing functions may be simplified, but electrical-photonic coordination remains critical.

CPO is often described as an optical technology, but it is also a packaging technology. The photonic engine, electrical ICs, fiber interfaces, laser source, and thermal path must work together as a system.

Testing is also harder than in a purely electrical device. Engineers must validate both optical and electrical performance: optical power, coupling loss, modulation behavior, receiver sensitivity, signal integrity, thermal behavior, and link reliability. At scale, this makes packaging and testing as important as chip design.

Market data shows why optical interconnect capacity has become strategically important, but the engineering case still depends on bandwidth density, power budget, reach, packaging feasibility, and system reliability. Forecasts can indicate demand pressure, but they do not prove that every optical architecture will scale at the same speed.

LightCounting reported that optical transceiver and related product sales reached $23.8 billion in 2025, up 55% from 2024. That growth reflects strong demand from data-center and AI infrastructure deployment, especially high-speed Ethernet optics and related products.

This does not mean every optical module category grows equally. It does show that the optical-electrical boundary has become a major infrastructure investment area as AI clusters expand.

Goldman Sachs Research has forecast that the AI networking total addressable market could increase by nine times to $154 billion by 2028, with CPO contributing a major portion of that opportunity. Such figures are best treated as scenario-based market estimates rather than direct evidence that every CPO architecture will be adopted at the same pace.

The engineering takeaway is more important than the headline number: as AI systems become denser and more distributed, the value of the interconnect layer rises. CPO, silicon photonics, external lasers, optical modules, fiber, and packaging all become more important because they sit directly in the path of AI data movement.

Optical interconnects matter because AI clusters are distributed systems. The more GPUs and switches a system uses, the more important data movement becomes.

Copper remains useful for short, controlled electrical paths, but it becomes harder to scale across longer high-speed links. Fiber provides reach, bandwidth, EMI immunity, and WDM-based capacity scaling.

Pluggable optical modules are still central to data-center networking. They provide a flexible and serviceable way to connect racks, switches, and systems. They will not disappear simply because CPO is emerging.

CPO is an architectural change, not just a smaller optical module. It moves optical conversion closer to the ASIC, often using silicon photonics PICs and external laser sources. Its value is strongest where bandwidth density and power pressure are most severe.

Silicon photonics is a bridge between electronics and optics, but it does not remove the need for compound-semiconductor light sources. InP lasers, SOI wafers, photonic integration, coupling, packaging, and testing all remain part of the system.

The optical interconnect supply chain is distributed. No single technology layer determines success. Materials, epitaxy, lasers, DSPs, silicon photonics, packaging, testing, modules, and fiber infrastructure all have to scale together.

Optical interconnects are high-speed data links that use light to move information between GPUs, switches, racks, and data-center systems. They help AI clusters exchange data over longer distances and higher bandwidths than copper can support efficiently at scale.

Fiber is not replacing copper everywhere. Copper remains useful for short internal links. Fiber becomes more attractive for rack-to-rack, switch-to-switch, and cluster-scale links because it provides longer reach, high bandwidth, EMI immunity, and better scalability through optical multiplexing.

A pluggable optical transceiver typically includes a laser source, modulator, photodetector, DSP, and optical coupling components. Together, these parts convert electrical signals into optical signals for fiber transmission, then convert received optical signals back into electrical data.

Pluggable optics are replaceable modules installed at the system edge. CPO moves optical engines closer to the ASIC package. Pluggable optics prioritize serviceability and flexibility, while CPO targets shorter electrical paths, higher bandwidth density, and lower power pressure in selected high-density links.

Silicon photonics can guide, split, modulate, and detect light, but silicon is inefficient as a light source. InP lasers are still needed to provide optical power, especially in architectures where a silicon photonics PIC handles modulation and routing while an external laser supplies continuous light.

CPO is unlikely to replace pluggable optics across all data-center links. The two architectures address different layers. CPO is suited to high-density chip-adjacent or switch-level optical integration, while pluggable optics remain useful for many rack, switch, and data-center interconnects.