With the global AI market on track to hit $738.8 billion by 2026, data centers are feeling the pressure. This kind of growth demands much faster data transfer and way lower energy use, two things that are often at odds. So, with Coherent’s optical solutions getting so much attention in these AI-heavy environments, what are they actually doing to solve these high-performance computing problems?
Key Takeaways
- To scale AI data center bandwidth, you need 800G and 1.6T transceivers, and deployments are going to pick up speed starting in 2025.
- Using silicon photonics can cut power consumption by up to 50% over old-school electrical interconnects, a direct attack on the massive energy needs of AI clusters.
- Moving to co-packaged optics (CPO) is expected to cut the networking space needed in a server rack by 20% to 30%, which helps with the physical layout and cooling.
- Modern optical management software gives you real-time network telemetry, which is what you need for predictive maintenance and juggling resources for AI jobs.
- The initial CapEx for optical solutions is higher, but the total cost of ownership is actually lower over five years in a hyperscale AI data center because you save so much on OpEx.
800G and 1.6T Optical Transceivers: The Bandwidth Imperative
A recent analysis from LightCounting Market Research shows shipments of 800G optical transceivers for data centers are set to more than double each year between 2024 and 2026. This is a fundamental change in how AI clusters talk to each other. Your standard copper cables just choke on the amount of data that modern AI, especially large language models, needs for training and inference. You can’t move that much data over copper without creating a massive bottleneck. So, these optical transceivers, especially at 800 gigabits per second (Gbps) and the upcoming 1.6 terabits per second (Tbps), are basically the only way to build the capacity needed.
In my own work with hyperscalers, the trend is obvious: compute isn’t the problem anymore, it’s moving data between GPUs and accelerators. Customers aren’t just buying for today, they’re demanding infrastructure that won’t be obsolete in a year. High-density, low-latency interconnects are what everyone is focused on. This is where products like Coherent’s 800G QSFP-DD transceivers come in, because they give you the throughput you need for all that GPU-to-GPU and GPU-to-memory traffic inside big AI pods. And 1.6T is already around the corner. We’re seeing prototypes that show it’s possible to push that much more data within the DC.
Power Efficiency Through Silicon Photonics: A 50% Reduction
An IEEE report from late 2025 pointed out that for short-reach connections in a data center, silicon photonics-based optical interconnects can cut power use by up to 50% compared to equivalent copper links. For AI data centers struggling with insane energy bills and heat problems, a number like that is a lifeline. A single AI rack full of high-performance GPUs pulls a crazy amount of power. Saving watts lowers operating expenses and cooling requirements, period.
It all comes down to moving data with light instead of electrons. Electrons moving through copper generate heat, and you have to spend money to cool that down, which of course uses more energy. Photons just don’t create that much heat. Coherent has poured a lot of money into silicon photonics technology, which is all about getting optical parts onto silicon chips for mass production. This gets costs down and lets you integrate everything more tightly, cutting signal loss and power draw. It’s a huge step that redefines the energy footprint of AI hardware. If you think copper has a future at AI scale, you’re not paying attention to the power bills. The economics just don’t work.
Co-Packaged Optics (CPO): Optimizing Rack Space by 20-30%
Analysts at places like Gartner are projecting that by 2028, adopting co-packaged optics (CPO) could shrink the space networking gear takes up in a rack by 20% to 30%. This detail really affects how you design and scale a data center. Right now, most setups use pluggable optical modules on the front of a switch. That whole assembly eats up board space and needs long electrical traces running to the switch’s main chip, which adds latency and burns power.
With CPO, you’re putting the optical transceiver right next to the switch ASIC (Application-Specific Integrated Circuit) in the same package. The electrical paths become incredibly short, which cuts power loss, cleans up the signal, and frees up all that front-panel real estate. In a hyperscale AI environment where you’re fighting for every square inch and every watt, getting 20% of your networking footprint back is a significant advantage. You can pack more compute into each rack, which might mean you need fewer racks for the same job, saving a ton on expensive data center floor space. It’s about redesigning the architecture from the ground up to remove these built-in inefficiencies.
Advanced Optical Network Management Software: Real-time Telemetry for AI
At the March 2026 Optical Fiber Communication Conference (OFC), a case study showed how one of the big cloud providers got a 15% bump in network utilization for its AI workloads just by deploying advanced optical network management software. They did it with real-time telemetry and dynamic path optimization. Static network configurations are obsolete for the spiky, unpredictable traffic that comes from AI training and inference.
Coherent’s management software isn’t just about basic monitoring. The platforms collect detailed data on everything from link performance and power levels to error rates across the whole optical fabric. Using that data, the system can predict bottlenecks and dynamically reroute traffic to keep latency low and throughput high. It also helps with preventative maintenance by flagging components that are about to fail. For AI jobs where a few milliseconds of extra latency can mess up model convergence times, this kind of intelligent, adaptive network management is a necessity. It’s how you make sure the physical network can keep up with the demands of the workload.
Disagreeing with Conventional Wisdom: The “Cost Barrier” Misconception
The common argument you always hear is that optical’s higher initial CapEx makes it a non-starter compared to copper. A lot of IT managers still grab copper for short-reach connections because the upfront cost looks lower. But that view completely misses the point about total cost of ownership (TCO) over the life of the gear. A late 2025 analysis from ACG Research showed that for hyperscale AI data centers, optical actually has a lower TCO over a five-year period because the operational savings are so significant.
Focusing solely on CapEx for AI infrastructure is a dangerous oversight. The money you save on power consumption and less cooling infrastructure, combined with higher network reliability, quickly offsets the initial investment. Considering the cost of downtime for an AI cluster or the performance hit from a slow interconnect, the slightly higher price for optical transceivers becomes negligible. Optical fiber’s longevity and better upgrade path also mean fewer costly rip-and-replace projects down the road. We need to shift the conversation to total cost of ownership. For AI, optical solutions are the answer.
The sheer demands of AI data centers are forcing a complete rethink of networking and infrastructure. Coherent’s optical solutions, their high-speed transceivers, their work in silicon photonics, and co-packaged optics, are part of what’s making this new scale possible, delivering the speed and efficiency that these AI workloads need to even run.
What is an 800G optical transceiver?
An 800G optical transceiver is a device for converting electrical and optical signals, letting you transfer data at 800 gigabits per second over fiber. They’re mostly used for high-bandwidth links within data centers.
How does silicon photonics contribute to energy efficiency in AI data centers?
Silicon photonics puts optical components right onto silicon chips, allowing you to send data using light instead of electricity. Because light-based transmission generates way less heat and uses less power than old electrical interconnects, it cuts a data center’s total energy use and reduces how much cooling you need.
What are co-packaged optics (CPO) and their benefits?
With co-packaged optics (CPO), the optical transceivers are put directly into the same package as a switch’s main ASIC. The main benefits are that it shortens electrical paths, which improves signal quality and lowers power use, and it frees up a lot of space on the switch. This can improve server rack density by 20% to 30%.
Why is real-time optical network management important for AI?
Real-time optical network management gives you detailed data on your network’s performance, so you can route traffic on the fly and perform predictive maintenance. For AI jobs that need super low latency and tons of bandwidth, this is how you make sure the network is being used efficiently, avoid bottlenecks, and generally make training and inference run better.
Are optical solutions more cost-effective than copper for AI data centers?
Yes. The upfront cost (CapEx) is often higher than copper, but for a big AI data center, optical has a lower total cost of ownership (TCO) over a five-year period. You make up the difference and then some through major savings on OpEx, things like lower power bills, reduced cooling costs, and better reliability over time.
