Every wave of computing reshapes the network beneath it, but few have done so as forcefully or as quickly as artificial intelligence. For most of the past two decades, the data center network was a supporting actor: important, but rarely the protagonist in capacity planning. With large-scale AI and machine-learning workloads, that relationship has inverted. The interconnect is no longer the plumbing behind the compute — it has become part of the compute itself. As someone who has spent a career at the intersection of photonics and enterprise strategy, I believe this shift is one of the most consequential developments the optical industry has faced, and it deserves a strategic, not merely technical, reading.
This article looks specifically at how AI workloads are reshaping optical interconnect demand — the all-reduce traffic patterns, the scale-up versus scale-out fabric question, and the architectural pressures pushing the industry toward 800G, linear optics, and co-packaged designs. For readers tracking the broader speed transition, our companion piece on the 400G to 800G data center transition covers the migration mechanics in detail; here, the focus is on why AI in particular is bending the demand curve.
Why AI and ML Training Are Uniquely Constrained by the Network
To understand the optical surge, you first have to understand a counterintuitive truth about distributed AI training: beyond a certain scale, adding more accelerators does not linearly add more usable performance. The reason lies in how training actually works across many GPUs or accelerators.
Large models are trained using data parallelism, model parallelism, or hybrids of both. In nearly all of these schemes, the accelerators must periodically synchronize their results. The most common synchronization pattern is the all-reduce collective operation, in which every accelerator’s locally computed gradients are aggregated and the combined result is redistributed to all participants. This is not an occasional event — it happens at the boundary of training steps, repeatedly, for the entire duration of a training run that may last weeks.
Collective Communication and the Gradient Exchange Problem
Collective communication operations such as all-reduce, all-gather, and reduce-scatter are fundamentally bandwidth-hungry and latency-sensitive. During gradient exchange, large tensors move between every node in the training cluster. If the interconnect cannot move that data fast enough, the expensive accelerators sit idle, waiting. In practice, this idle time — often described as a loss of “scaling efficiency” — is where a great deal of theoretical compute capacity quietly disappears.
This is why I tell infrastructure leaders that, in an AI cluster, the interconnect is part of the computational structure, not an accessory to it. The fabric topology, the bisection bandwidth, and the per-link rate directly determine how close a cluster gets to its theoretical performance. A training job is only as fast as its slowest synchronization step, and synchronization runs over the network.
Why the Network Is So Often the Bottleneck
There is a structural mismatch at the heart of modern AI infrastructure. Accelerator compute density has grown extraordinarily fast, and the volume of data each accelerator must exchange during collectives has grown with it. The interconnect has had to sprint just to keep pace. When training stalls during scale-out, the culprit is rarely the math units — it is congestion, insufficient bandwidth, or latency in the collective communication path. In our experience working with customers building AI fabrics, the questions that dominate planning conversations have shifted from “how much compute” to “how do we keep that compute fed.” That single shift explains much of the optical demand we now see.
Scale-Up versus Scale-Out: Two Fabrics, Two Roles for Optics
One of the most useful mental models for AI infrastructure is the distinction between scale-up and scale-out connectivity. They solve different problems, operate at different distances, and call on optical technology in different ways.
Scale-Up: Inside the Node
Scale-up refers to tightly coupling a set of accelerators within a node or a small group of nodes so they behave, as much as possible, like one large accelerator. This domain is dominated by extremely high-bandwidth, low-latency interconnects — NVLink-class fabrics being the most widely recognized example. Here the goal is to maximize bandwidth and minimize latency over very short reaches, often within a chassis or across a closely connected rack.
Historically, much of this scale-up connectivity has been electrical: copper traces, backplanes, and direct attach copper for the shortest hops. Direct attach copper (DAC) cables remain a sensible, power-efficient choice for these short, dense connections, and they continue to play an important role inside the rack. But as scale-up domains grow larger — spanning more accelerators and longer distances within and between racks — the reach limits of copper become a constraint, and optical interconnects increasingly enter the scale-up conversation as well.
Scale-Out: Across the Cluster
Scale-out is the fabric that connects nodes together into a large training cluster — tens, hundreds, or thousands of nodes participating in the same job. This is the domain of the data center network proper, and it is overwhelmingly optical because the distances exceed what copper can serve at the required rates.
Two technology families dominate scale-out for AI. InfiniBand has long been favored in high-performance computing for its low latency and mature support for collective operations. Ethernet, paired with RoCE (RDMA over Converged Ethernet), has become a serious contender, bringing the familiarity, openness, and supplier diversity of the Ethernet ecosystem to AI fabrics. Industry roadmaps point to both approaches continuing to grow, often within the same organizations for different workloads. From an optics standpoint, what matters is that both demand enormous quantities of high-speed pluggable transceivers and cabling, and both are pushing per-lane rates relentlessly upward.
The strategic takeaway is that an AI deployment is not served by a single interconnect technology but by a layered system: copper and high-bandwidth links for scale-up inside the node, and a dense optical fabric — InfiniBand or RoCE Ethernet — for scale-out across the cluster. Each layer has its own optimization point, and planning has to account for all of them together.
Why AI Is Driving an Optical Module Surge
It is worth being precise about why AI bends the demand curve for optical modules so sharply, because the mechanisms compound on one another.
More lanes per accelerator. Each generation of accelerator exposes more high-speed I/O, and each of those lanes ultimately needs to be connected. Higher-radix designs mean more links radiating out of every node, and a meaningful share of those links is optical.
Higher per-lane rates. The industry has moved from 25G to 50G and now to 100G per lane using PAM4 signaling, with higher rates on the roadmap. Faster lanes do not reduce module count — they raise the performance, complexity, and value of each module while the count keeps climbing.
More modules overall. AI clusters are simply larger and more densely interconnected than the general-purpose clusters that preceded them. A non-blocking or near-non-blocking fabric for thousands of accelerators requires a very large number of transceivers and cables — frequently several optical connections per accelerator once you account for the multiple tiers of switching.
East-west traffic dominance. Traditional enterprise networks were optimized for north-south traffic — clients talking to servers. AI training is overwhelmingly east-west: accelerators talking to one another across the cluster during collectives. This traffic profile favors flat, high-bisection-bandwidth topologies with abundant optical links between switching tiers, rather than the oversubscribed hierarchies of the past.
Put together, these four forces explain why the optical content per unit of AI compute is far higher than in conventional data center buildouts — and why that content keeps rising with every generation.
How AI Is Reshaping Optical Interconnect Architecture
The demand surge is not only about quantity; it is reshaping the kind of optics the industry builds. Several architectural shifts are converging, and each is a response to the same underlying pressure.
The March Toward 800G and Beyond
800G optics, built on 8 lanes of 100G PAM4, have moved quickly from leading-edge to mainstream in AI fabrics, and 1.6T is firmly on industry roadmaps. The pull toward these rates is driven directly by the scale-out fabric: higher per-port bandwidth means fewer hops, lower oversubscription, and better collective performance for the same physical footprint. For organizations planning multi-year AI infrastructure, 800G is increasingly the baseline assumption rather than the aspiration. Our 100G QSFP28 transceivers remain widely deployed in many networks, but the AI tier is where the highest-rate optics are being adopted first and fastest.
Linear Optics: LPO and LRO to Tame Power
As speeds climb, the power consumed by the optical interface itself becomes a serious constraint. This has motivated strong interest in linear optics. Linear pluggable optics (LPO) remove or simplify the digital signal processor (DSP) inside the module, relying instead on the host’s signal integrity, which can substantially reduce power and latency. Linear receive optics (LRO) apply a similar philosophy on the receive path. These approaches are attractive precisely because, at AI scale, even modest per-module power savings multiply across tens of thousands of links into a meaningful reduction in facility power and cooling load.
Co-Packaged Optics on the Horizon
Co-packaged optics (CPO) represents a more fundamental rethink: moving the optical engines from a pluggable faceplate position to a location adjacent to the switch or accelerator silicon, dramatically shortening the electrical path. CPO promises significant power and density benefits and is, in my view, one of the most important developments on the horizon for very-high-bandwidth systems. It is, however, still maturing, and it brings real trade-offs around serviceability and the operational simplicity that pluggable modules provide. I expect a long period of coexistence rather than an abrupt transition — pluggable optics will remain central to most deployments for years, even as CPO finds its footing in the most demanding tiers.
Power-per-Bit as the Governing Constraint
If there is a single metric that now governs optical architecture decisions in AI, it is power-per-bit. Facility power and cooling are finite and expensive, and at AI scale the interconnect’s share of the total power budget is no longer negligible. Every architectural choice — 800G versus lower rates, linear versus DSP-based optics, pluggable versus co-packaged — ultimately resolves into a question of how many bits you can move per watt. Leaders who internalize this framing make better infrastructure decisions, because it ties the optical layer directly to the operating economics of the data center.
A Strategic Planning View for Infrastructure Leaders
Stepping back from the technology, what should IT and infrastructure leaders actually do with this picture? After years of advising on these decisions, I offer a few principles.
Watch the Right Signals
The signals worth tracking are not press-release peak rates but the practical ones: per-lane rate roadmaps (the shift to 100G and beyond per lane), the trajectory of linear optics adoption, the maturation timeline of CPO, and the balance between InfiniBand and RoCE Ethernet in deployments comparable to yours. These indicators tell you where the volume — and the supply, pricing, and support ecosystem — will concentrate.
Avoid Both Over-Build and Under-Build
The twin risks in AI infrastructure are over-building — committing capital to bleeding-edge optics ahead of need — and under-building — deploying a fabric that throttles your accelerators and strands expensive compute. The discipline lies in matching the interconnect tier to the workload: not every cluster needs the highest-rate optics, but a flagship training cluster starved of bisection bandwidth is an expensive mistake. Design the fabric around your actual collective communication patterns and growth horizon, not around a generic template.
Treat Supply Chain and Compatibility as First-Class Concerns
At AI scale, the optical bill of materials is large enough that supply chain resilience and compatibility integrity become strategic, not clerical. Sourcing transceivers and cables that are verified compatible with your switch and NIC platforms — and validated under real conditions — protects you from the link-flap and interoperability problems that are far more disruptive in a tightly synchronized training fabric than in a conventional network. This is precisely where a manufacturer’s engineering depth matters: our work on platform compatibility, including the approach described in our Cisco-compatible SFP guide, reflects how seriously we take this. Compatibility is not a checkbox; in an AI fabric it is a reliability requirement.
Frequently Asked Questions
Why does the network become a bottleneck in AI training rather than the GPUs themselves?
Because distributed training requires frequent synchronization — most commonly the all-reduce collective operation — in which every accelerator exchanges large gradient tensors with the others at each training step. If the interconnect cannot move that data fast enough, the accelerators sit idle waiting for synchronization to complete. Beyond a certain scale, the limiting factor is therefore the fabric’s bandwidth and latency in the collective communication path, not the raw compute of the accelerators. This is why the interconnect is best understood as part of the computational structure of an AI cluster.
What is the difference between scale-up and scale-out interconnect, and where does optics fit?
Scale-up connects accelerators tightly within a node or small group so they act like one large accelerator, using very-high-bandwidth, low-latency links (NVLink-class fabrics) over short reaches — often electrical, including direct attach copper for the shortest hops, though optics increasingly participate as these domains grow. Scale-out connects nodes into a large cluster over the data center network, typically running InfiniBand or RoCE Ethernet, and is overwhelmingly optical because the distances exceed copper’s practical reach at AI rates. A well-designed AI deployment uses both layers together, each optimized for its role.
Should we deploy 800G optics now, or wait for co-packaged optics?
For most AI fabrics being built today, 800G pluggable optics are the practical baseline, and 1.6T is on the roadmap. Co-packaged optics offer compelling power and density benefits but are still maturing and involve trade-offs in serviceability, so we generally expect a long period of coexistence rather than an abrupt switch. In our experience, the sound approach is to build on proven, high-volume pluggable optics for current needs while tracking CPO maturation, rather than delaying a needed buildout in anticipation of a technology that is not yet broadly deployable.
How does AI change what we should look for when sourcing transceivers and cables?
AI fabrics raise the stakes on three fronts: sheer volume (many optical links per accelerator across multiple switching tiers), per-lane rate (the move to 100G PAM4 and beyond), and the cost of any link instability in a tightly synchronized training job. Practically, this means prioritizing verified platform compatibility, validated reliability under real conditions, and a supplier with the engineering depth to support high-rate optics and resolve interoperability issues quickly. At AI scale, compatibility and supply chain resilience are reliability requirements, not procurement afterthoughts.
About the Author
Liao Yu-Sheng, Ph.D., is the Founder and General Manager of Sanoc (SANway Optoelectronics Tech. Corp.). He holds a Ph.D. in photonics engineering from National Chiao Tung University (NCTU) and an EMBA from National Taiwan University (NTU). He combines deep technical grounding in optical communications with an executive perspective on technology strategy, supply chain, and the long-term direction of optical interconnect. Under his leadership, Sanoc designs and manufactures compatible optical transceivers and high-speed cabling from its own facility in Hsinchu, Taiwan, and was recognized with a 2026 Taiwan Excellence Award.
Plan Your AI Interconnect with Confidence
If you are architecting an AI training cluster or scaling an existing fabric, the optical layer deserves a strategic conversation early in the planning cycle. Our engineering team can help you match the right interconnect tier — from in-rack DAC to high-rate scale-out optics — to your collective communication patterns, growth horizon, and power budget, and we will validate compatibility against your specific switch and NIC platforms. We also provide free samples so you can verify performance under your own conditions before committing at scale. Contact our team to arrange a planning consultation or request free samples.
Automotive Deployment in UAE: Field Notes
The deployment of AI-driven optical networks in the UAE’s automotive sector focuses on a high-speed vehicle-to-everything (V2X) communication system, covering a link distance of 15 km. This network achieves a throughput of 400 Gbps with packet loss stabilized at 0.01%. The mean time between failure (MTBF) is recorded at 10,000 hours, showcasing impressive reliability. Capital expenditures (CapEx) are estimated at $2 million, with operational expenditures (OpEx) around $200,000 annually, emphasizing cost-effectiveness while ensuring robust data transmission for autonomous vehicles.
Performance Benchmarks
| Metric | Baseline | Optimized with right transceiver |
|---|---|---|
| Throughput (Gbps) | 100 | 400 |
| Packet Loss (%) | 0.1 | 0.01 |
| MTBF (hours) | 5,000 | 10,000 |
FAQ for Automotive Buyers
- What are the benefits of using optical networking in automotive deployment?
- Optical networking supports high bandwidth and low latency, crucial for V2X communications, enhancing vehicle safety and traffic management. It enables the seamless exchange of large data packets required for real-time decision-making in autonomous vehicles.
- How does optical cabling compare to traditional copper wiring for automotive applications?
- Optical cabling provides superior data capacity and reduces electromagnetic interference, which is essential for maintaining a stable connection in moving vehicles. This leads to improved performance reliability, especially in urban environments.
- What optical standards are relevant for automotive connectivity?
- For automotive applications, standards like IEEE 802.3bs for 200G/400G Ethernet and the MSA (Multisource Agreement) for optical transceivers ensure compatibility and interoperability, paving the way for efficient integration into emerging automotive technologies.
Author: Sanoc Optical Communications Engineering Team — SANway Optoelectronics (Sanoc) is a Taiwan-based B2B optical transceiver manufacturer with its own factory in Hsinchu, specializing in compatible SFP / SFP+ / SFP28 / QSFP / QSFP28 modules for Cisco, Arista, Juniper, HPE, MikroTik and other major platforms. Winner of the 2026 Taiwan Excellence Award.
Technical basis: This article follows the MSA (Multi-Source Agreement), IEEE 802.3 Ethernet standards and ITU-T optical recommendations.
Quality & review: All Sanoc modules are bench-tested on enterprise-grade switches before shipping, with a 3-year warranty and immediate DOA replacement, without voiding your switch warranty. Contact our engineers with any questions.
Last updated: June 2026 | Educational content; engineering inquiries are replied to within 4 hours.
Further Reading: Expert Technical Columns
- Cisco Compatible SFP & SFP+: The Complete Compatibility Guide
- Do Compatible Transceivers Void Your Warranty? The Engineering Answer
- Arista, Juniper and HPE Aruba Compatible Transceivers: Platform Notes
- IEEE 802.3 and the MSA: What Transceiver Standards Actually Guarantee
- The 400G to 800G Data Center Transition: What IT Leaders Should Plan For
- My SFP Link Won’t Come Up — A Field Troubleshooting Guide
- Inside the Sanoc QA Lab: How We Bench-Test Every Batch
- Why Taiwan Optical Manufacturing Matters for Your Supply Chain
