The crushing demand from AI for more computational power is breaking current hardware. Foundational as they are, traditional electrical interconnects are hitting a wall as data rates and processor counts explode. This is exactly the problem that Lumentum’s optical interconnects are built to solve, overcoming these limitations to unleash next-gen AI hardware. But can optical solutions actually deliver the scalability and efficiency that AI needs to keep growing?
Key Takeaways
- Lumentum’s optical interconnects get around the physical limits of copper wiring in high-performance computing, giving you much more bandwidth for a lot less power.
- Switching to optics is how AI accelerators and data centers will scale, allowing for denser chip designs and the longer connectivity runs needed for massive AI models.
- Vertical Cavity Surface Emitting Lasers (VCSELs) and silicon photonics are the two technologies making optical interconnects practical for AI, enabling them to be integrated right on or next to the processors.
- Any company building out serious AI infrastructure has to look at the total cost and performance jump from optical interconnects, because they’re on track to be standard for large AI builds by 2028.
- Ongoing work in co-packaged optics and photonic integrated circuits will bring even more integration and efficiency, making optical I/O essential for future AI development.
The Looming Bottleneck: Why Electrical Interconnects Fall Short for AI
AI models, especially the huge language models and deep neural networks we see today, depend on moving an unbelievable amount of data between processors, memory, and storage. Modern AI accelerators like GPUs and custom ASICs are monsters of computation, but their real-world performance is often limited not by their processing ability, but by how fast they can talk to each other. This is the interconnect bottleneck. Electrical signals running over copper traces have physical limits that are becoming a massive problem at the multi-terabit per second speeds that AI workloads now require.
As you push frequency higher or send signals over longer distances, signal integrity on copper just falls apart because of resistance, capacitance, and inductance. This signal loss forces you to add complex equalization circuits, which burn a ton of power and add latency. On top of that, the physical bulk of electrical traces and their connectors limits how many connections you can cram onto a single chip or into a server rack. In a real-world HPC cluster training a complex AI model, you might have hundreds or thousands of compute nodes that all need to communicate instantly. If the interconnects can’t keep up, the processors sit idle and your whole system’s efficiency tanks. We see this firsthand in new data center designs where the power budget for just moving data around is starting to rival the power used by the processors themselves. This is unsustainable.
The heat generated by high-speed electrical signaling is another huge challenge. More data, more speed, more heat. This requires complex and expensive cooling systems that add to your opex and design complexity. At its core, electrical signals are bound by the physics of electrons moving through metal. Light, however, works differently, and that’s why it’s such a strong alternative for moving data around inside AI infrastructure.
Lumentum’s Optical Solutions: A New Model for Data Movement
Lumentum is developing the optical tech that’s set to change how AI hardware communicates. Their work on optical interconnects uses light instead of electricity for data transmission, which completely changes the performance equation. The benefits include much higher bandwidth, lower power consumption, and less latency. Instead of electrons fighting their way through copper, photons zip through optical fibers or waveguides, and they’re pretty much immune to the electromagnetic interference and signal loss you see over typical data center distances.
A key piece of Lumentum’s strategy is their **Vertical Cavity Surface Emitting Lasers (VCSELs)**. These are tiny, efficient lasers perfect for short-reach optical links, like the ones you need inside a server rack or even from one chip to another. You can modulate VCSELs at incredibly high speeds to get multi-gigabit rates per channel, and because they’re so small, you can pack them in densely. A report from LightCounting Market Research confirms this, projecting huge growth for VCSELs in 800G and 1.6T transceivers by 2027, almost all of it driven by AI. This shows the industry gets it. Being able to cram more data channels into a smaller space without the heat and power penalty of copper is a massive win for AI accelerators that require massive parallelism.
Beyond VCSELs, Lumentum is also a major force in **silicon photonics**. This tech puts optical components right onto a silicon wafer with standard semiconductor manufacturing methods. It lets them build complex photonic integrated circuits (PICs) that can put lasers, modulators, and detectors all on one chip. This integration is important for the next step: co-packaged optics, where the optical transceiver sits right next to the AI processor in the same package. This drastically shortens the distance electrical signals have to go, putting the optical conversion point right next to the processor and killing the electrical bottleneck. The goal is to get rid of the clunky pluggable transceivers we use today for this kind of communication and move to a much smaller, more efficient optical engine. This fundamentally rethinks the architecture of high-performance AI systems.
The Impact on AI Hardware Architecture and Scalability
Bringing Lumentum’s optical interconnects into the mix has huge consequences for how we design and scale AI hardware. The most significant impact is on chip-to-chip and board-to-board communication. As AI models get more complex with more parameters and bigger datasets, the number of processors you need to connect grows exponentially. Trying to do that with traditional electrical backplanes and cables gets messy, power-hungry, and bandwidth-limited fast.
Optical interconnects solve this by enabling far denser connectivity. In a rack full of AI accelerators, electrical links mean each card has a limited number of high-speed ports because of space and power limits. With optics, a single fiber can carry terabits of data, blowing up the total bandwidth available between components and allowing for more flexible network topologies inside the server and across racks. For example, those upcoming 1.6 terabit per second (Tbps) optical transceivers, which we’ll see a lot more of in 2026, will let AI clusters scale way beyond what’s possible with current 400G or 800G electrical links without insane power and space costs. This capability reshapes the basic limits of how many GPUs or NPUs can actually work together on one AI job.
Plus, optics make **disaggregated infrastructure** a practical reality. Instead of having compute, memory, and storage stuck together, high-bandwidth optical links let you separate them physically while keeping them logically connected with super low latency. This allows data centers to allocate resources much more flexibly, provisioning them exactly where they’re needed instead of being stuck with what’s physically nearby. This is a huge benefit for AI, where workloads are dynamic and can demand sudden bursts of compute or massive data dumps to memory pools. The ability to scale compute and memory on their own, all tied together with a high-speed optical fabric, is foundational for the next generation of hyperscale AI deployments.
Power Efficiency and Thermal Management: Critical for Sustainable AI
AI infrastructure’s power consumption and the heat it generates are critical, often-underestimated problems. Training a big AI model can pull megawatts of power, and a lot of that is just wasted as heat. High-speed electrical interconnects are a big part of that power draw and heat problem. Here, optical interconnects are just plain more **power-efficient** for high-bandwidth data transmission.
Think about the power it takes to push an electrical signal across a board versus modulating a laser to send light through a fiber. The power-per-bit for optical links is way lower, especially as the distance gets longer. This directly reduces data center operating costs and eases the strain on power grids. A study from the Optical Internetworking Forum (OIF) showed that next-gen optical modules are targeting power efficiencies much better than their electrical equivalents for the same bandwidth. These reductions can lower the overall power footprint of an AI supercluster by double-digit percentages, which is a massive saving for operators.
Less power draw also means less heat. This has a direct effect on **thermal management strategies** inside the data center. Less heat from your interconnects means you don’t need as much energy-guzzling cooling infrastructure. Optical interconnects reduce power, which reduces heat, which in turn reduces the power needed for cooling. For AI data centers facing power density issues and rising energy costs, this efficiency gain is becoming a mandatory requirement for sustainable growth. My own experience consulting on data center design confirms that thermal budgets are now as important as compute capacity when planning new AI deployments, and operators are looking hard for solutions to reduce power, optical interconnects are a key part of that effort.
Future Outlook: Co-Packaged Optics and Beyond
The path for optical interconnects in AI is clearly heading toward deeper integration and even better performance. The next big thing on the horizon is co-packaged optics (CPO). This means putting the optical transceivers directly into the same package as the AI processor instead of having them as separate pluggable modules. By getting the optics right next to the compute die, you shrink the length of high-speed electrical traces to almost nothing, which minimizes signal loss, power draw, and latency. You end up with a true “optical engine” right at the heart of the AI chip. Lumentum and other industry leaders are working on the components and manufacturing to make CPO a reality for high-volume AI.
CPO for AI offers unparalleled bandwidth density, ultra-low power for the interconnects, and a much smaller physical footprint. This lets you pack AI accelerators much more densely, building more powerful systems in smaller spaces. CPO will likely start in niche, high-end AI training systems, but the tech will spread to broader AI inference and general computing as the costs drop and manufacturing gets better. The widespread adoption of CPO in AI infrastructure is expected around 2028-2029, which represents a fundamental change in how AI hardware gets designed.
Looking past CPO, research is already happening on even more advanced photonic integrated circuits (PICs) that could blur the line between communication and processing. While full optical computing is still a long way off, the constant innovation in optical interconnects from companies like Lumentum ensures that the physical foundation of AI infrastructure will keep evolving to handle the insane demands of artificial intelligence. AI’s advancement relies on its underlying hardware capabilities, and optical interconnects are proving to be the technology that lets us keep pushing those boundaries.
Lumentum’s optical interconnects represent a fundamental shift in how AI hardware will be designed and scaled. By solving the core problems of electrical signaling, these optical solutions will deliver a new level of performance, power efficiency, and scalability, making them a foundational technology for the next generation of artificial intelligence.
What are optical interconnects and why are they important for AI?
They use light to transmit data instead of electricity. They’re important for AI because they provide much higher bandwidth at lower power and with less latency than traditional copper connections, which helps break through the data bottlenecks that choke high-performance AI systems.
How do Lumentum’s VCSELs contribute to AI hardware performance?
Lumentum’s Vertical Cavity Surface Emitting Lasers (VCSELs) are small, efficient lasers designed for short optical links inside servers or between chips. They enable extremely high data rates per channel and can be packed densely, letting AI accelerators talk to each other much faster without the heat and power problems of electrical wiring.
What is silicon photonics and its role in future AI systems?
Silicon photonics is a technology for building optical components directly on a silicon wafer, using normal semiconductor manufacturing. It’s essential for future AI systems because it enables co-packaged optics, placing optical I/O right next to the AI processor in the same package, which drastically cuts down on slow electrical paths and boosts efficiency.
How do optical interconnects improve power efficiency and thermal management in AI data centers?
They use significantly less power to transmit each bit of data compared to electrical links, especially over any real distance. This directly cuts the power bill for an AI data center and, just as important, reduces the amount of heat generated. Less heat means you need less power for cooling, creating major operational savings and enabling more sustainable AI infrastructure.
What is co-packaged optics (CPO) and when is it expected to become widespread in AI?
Co-packaged optics (CPO) is the practice of integrating optical transceivers directly into the same physical package as an AI processor to make the electrical connections as short as possible. It delivers incredible bandwidth density and power efficiency. We expect to see widespread CPO adoption in high-end AI training systems starting around 2028-2029.
