Optical Networking in the AI Era: The Backbone of Next-Generation Connectivity

Abstract

The exponential growth of artificial intelligence (AI) workloads, from large language models (LLMs) to generative AI applications, has created an unprecedented demand for high-bandwidth, low-latency, and energy-efficient networking infrastructure. As enterprises and hyperscalers grapple with processing massive datasets and supporting distributed AI clusters, optical networking has emerged as the only technology capable of delivering the capacity, speed, and scalability required to bridge data centers, servers, routers, and switches. This blog explores the transformative role of optical networking in the AI era, delving into key technological breakthroughs—including optical circuit switching (OCS), high-speed Ethernet (400G/800G/1.6T), and innovations like Linear Drive Pluggable (LPO) and Co-Packaged Optics (CPO)—while addressing the industry’s most pressing challenges: energy efficiency, cost scalability, and infrastructure compatibility. Drawing on insights from industry leaders (Google, Cisco, Nvidia, Broadcom) and market data from Dell’Oro Group, IDC, and Gartner, we examine how optical networking is evolving to meet the demands of AI-driven ecosystems, predict future trends (3.2T optics, standardized interoperability, AI-optimized clusters), and highlight the strategies vendors and enterprises are adopting to stay ahead in this rapidly changing landscape.

Table of Contents

• Introduction: AI’s Irreversible Impact on Networking
• Why Optical Networking Is Non-Negotiable for AI
• Key Technological Breakthroughs Reshaping Optical Networking
  • 3.1 Optical Circuit Switching (OCS): Google’s Apollo and the Future of Data Center Connectivity
  • 3.2 High-Speed Ethernet: From 400G to 1.6T and Beyond
  • 3.3 Standardization: 400ZR, OpenZR+, and the Race for Interoperability
• The AI Cluster Explosion: Bandwidth Demands at Exponential Scale
• The Great Optical Debate: LPO vs. CPO—Which Will Dominate AI Networks?
  • 5.1 Linear Drive Pluggable (LPO): Simplifying Deployment with Lower Power
  • 5.2 Co-Packaged Optics (CPO): Integration for Extreme Density
  • 5.3 A Comparative Analysis: Tradeoffs, Adoption Timelines, and Use Cases
• Industry Challenges: Energy, Cost, and Infrastructure Upgrades
• Vendor Strategies: How Cisco, Nvidia, Broadcom, and Others Are Leading the Charge
• Future Trends: 3.2T Optics, AI-Optimized Optical Networks, and the Next Frontier
• Conclusion: Optical Networking as the Backbone of AI’s Next Chapter

1. Introduction: AI’s Irreversible Impact on Networking

The past decade has witnessed a seismic shift in computing paradigms, driven by the rise of artificial intelligence. What began as experimental machine learning models has evolved into a global ecosystem of AI-powered applications—from generative AI tools like ChatGPT and DALL-E to industrial AI for manufacturing, predictive analytics for healthcare, and autonomous systems for transportation. These applications share a common trait: they demand unprecedented levels of computing power, data throughput, and distributed connectivity.

For enterprises and hyperscalers (Google, Amazon, Microsoft, Meta), the challenge is no longer just processing data—it’s moving it. AI workloads require massive datasets to be transferred between data centers, distributed across GPU clusters, and analyzed in real time. A single LLM training run, for example, can involve petabytes of data and thousands of GPUs working in parallel. Traditional networking technologies—such as copper-based Ethernet or legacy fiber solutions—are ill-equipped to handle this scale. They lack the bandwidth to support terabit-per-second (Tbps) data rates, the latency to enable real-time GPU-to-GPU communication, and the energy efficiency to power 24/7 AI operations without unsustainable costs.

This is where optical networking steps in. Fiber optics, which transmit data using light signals, have long been recognized for their superior bandwidth, low latency, and resistance to electromagnetic interference. But the AI revolution has elevated optical networking from a “nice-to-have” to a “must-have.” As Bill Gartner, Senior Vice President and General Manager of Cisco’s Optical Systems and Optics Group, puts it: “At the end of the day, fiber is the only connectivity technology capable of delivering the capacity organizations need over the distances required—connecting data centers, servers, routers, switches, and all the distributed components that make up today’s network architectures.”

In this blog, we will explore how AI is reshaping optical networking, from the adoption of 800G/1.6T Ethernet to the rise of optical circuit switching and the battle between LPO and CPO. We will examine the challenges the industry faces—energy consumption, cost scalability, infrastructure upgrades—and how vendors and enterprises are addressing them. Most importantly, we will highlight why optical networking is not just enabling AI, but defining its future.

1.1 The Scale of AI’s Networking Demand

To grasp the urgency of optical networking’s role, consider the scale of modern AI workloads:

• Data Volumes: A single training run for GPT-4 is estimated to have used over 100 petabytes of data. Transferring this data across a traditional 100G Ethernet network would take months; with 800G optical links, it can be done in weeks.
• GPU Clusters: AI training requires thousands of GPUs to communicate in parallel. A cluster with 32,000 GPUs (like those used by Meta and Google) needs inter-GPU bandwidth of up to 10 Tbps per link to avoid bottlenecks.
• Distributed Computing: Hyperscalers are increasingly using distributed data centers to reduce latency and improve resilience. For example, Google’s data centers in North America are connected by terabit-scale fiber links to enable global AI model training.

These demands are not static. According to Sameh Boujelbene, Vice President at Dell’Oro Group, “The size of emerging AI applications appears to be growing exponentially, with the number of parameters these applications need to process increasing 1,000x every 2 to 3 years.” This exponential growth means that today’s cutting-edge 800G networks will be obsolete in five years, driving the need for 1.6T, 3.2T, and beyond.

1.2 The Limitations of Traditional Networking

Traditional networking technologies cannot keep pace with AI’s demands:

• Copper Ethernet: Copper-based links (Cat 6a, Cat 7) are limited to bandwidths of up to 100G over short distances (≤100 meters) and suffer from signal degradation and high latency. They are unsuitable for data center interconnect (DCI) or long-haul AI cluster connectivity.
• Legacy Fiber Optics: Early fiber solutions (10G, 40G) lack the bandwidth to support Tbps-scale data rates. They also use power-hungry components (e.g., digital signal processors) that make large-scale deployment cost-prohibitive.
• Electronic Packet Switches (EPS): EPS, the backbone of traditional data center networks, consume massive amounts of power. For example, a single 100G EPS switch can use 500+ watts of power, making it impractical for large AI clusters with thousands of links.

Optical networking addresses these limitations by leveraging the unique properties of light:

• Bandwidth: Fiber optics can support terabits of data per second over a single strand of fiber, far exceeding copper’s capabilities.
• Latency: Light travels at nearly the speed of light (299,792 km/s), resulting in latency as low as 1ms per 100km—critical for real-time AI operations.
• Energy Efficiency: Optical components (e.g., transceivers, switches) use significantly less power than electronic alternatives. For example, an 800G optical transceiver uses ~15 watts, compared to 30+ watts for a copper-based equivalent.

As we delve deeper into this blog, we will see how these properties are being harnessed to build AI-optimized optical networks—and why this technology is the only viable path forward for the AI era.

2. Why Optical Networking Is Non-Negotiable for AI

The case for optical networking in AI is built on three foundational pillars: bandwidth, latency, and energy efficiency. Together, these pillars address the core challenges of AI workloads and explain why fiber optics have become the de facto standard for hyperscalers and enterprises alike.

2.1 Bandwidth: The Lifeblood of AI

AI workloads are bandwidth-hungry by design. Whether it’s training a large language model (LLM) or running inference for a generative AI application, data must flow freely between GPUs, storage systems, and data centers. Consider the following scenarios:

• GPU-to-GPU Communication: During training, GPUs in a cluster exchange intermediate results (activations, gradients) hundreds of times per second. A cluster with 1,000 GPUs requires a total inter-GPU bandwidth of ~100 Tbps to avoid bottlenecks.
• Data Center Interconnect (DCI): Hyperscalers use distributed data centers to reduce latency and improve fault tolerance. For example, Google’s AI models are trained across data centers in California, Oregon, and Washington, connected by 800G+ optical links.
• Edge-to-Cloud Connectivity: Edge AI applications (e.g., autonomous vehicles, smart cities) generate massive amounts of data that must be transmitted to cloud data centers for processing. Optical links enable this transmission at Tbps-scale rates.

Fiber optics are uniquely capable of meeting these bandwidth demands. Unlike copper, which is limited by electrical signal degradation, fiber can support multiple terabits per second over a single strand using wavelength-division multiplexing (WDM)—a technology that splits light into multiple wavelengths (colors) to transmit multiple data streams simultaneously. For example, a single fiber strand using dense WDM (DWDM) can support up to 80 channels of 100G, resulting in 8 Tbps of total bandwidth.

This bandwidth scalability is critical as AI models grow larger. Boujelbene notes that “AI cluster sizes (measured by the number of accelerators) are quadrupling every 2 years, from a typical 256 to 1,000, then quickly to 4K, and now some clusters have 32K and 64K accelerators.” Each new generation of AI clusters requires a corresponding increase in network bandwidth, and only optical networking can deliver this scale.

2.2 Latency: Enabling Real-Time AI Operations

Latency—the time it takes for data to travel from one point to another—is a make-or-break factor for AI. For example:

• LLM Inference: A user query to ChatGPT requires data to be processed by GPUs and returned in milliseconds. Even a 10ms increase in latency can degrade the user experience.
• Autonomous Vehicles: Self-driving cars need to process sensor data and make decisions in real time. Latency above 50ms can lead to accidents.
• Distributed Training: GPUs in a cluster must synchronize their work with minimal delay. High latency can slow down training times by 50% or more.

Optical networking minimizes latency by leveraging the speed of light. While electronic signals travel at ~2/3 the speed of light in copper, light signals in fiber travel at ~99% the speed of light. This difference may seem small, but over long distances (e.g., 1,000km), it translates to a latency reduction of ~10ms—critical for distributed AI operations.

Additionally, optical switches (e.g., OCS) reduce latency by eliminating the need for packet processing. Traditional electronic switches must inspect and route each packet individually, adding microseconds of delay per packet. Optical switches, by contrast, establish a direct light path between two points, enabling “cut-through” routing with latency as low as 100 nanoseconds per hop.

2.3 Energy Efficiency: Reducing AI’s Carbon Footprint

AI is not just computationally intensive—it’s energy intensive. A single LLM training run can consume as much energy as 120 American households use in a year. For hyperscalers running thousands of AI workloads, energy costs and carbon emissions are a major concern.

Optical networking addresses this by being significantly more energy-efficient than electronic alternatives:

• Transceivers: An 800G optical transceiver uses ~15 watts of power, compared to 30+ watts for a copper-based 100G transceiver. For a data center with 10,000 transceivers, this translates to annual energy savings of ~1.3 million kWh.
• Switches: Optical circuit switches (OCS) use 50–70% less power than electronic packet switches. Google’s Apollo OCS platform, for example, reduced power consumption in its data center networks by 30% compared to traditional EPS.
• WDM Technology: WDM enables multiple data streams to be transmitted over a single fiber strand, reducing the number of fibers and components needed—further lowering energy use.

As enterprises and governments push for carbon neutrality, energy efficiency is no longer a secondary consideration—it’s a business imperative. Optical networking allows organizations to scale their AI operations without sacrificing sustainability.

2.4 The Cost Argument: Scalability Without Breaking the Bank

While the upfront cost of optical networking can be higher than traditional solutions, its long-term scalability makes it more cost-effective for AI. Here’s why:

• Bandwidth per Dollar: Fiber optics deliver more bandwidth per dollar than copper. For example, a 100G copper link costs ~$10 per Gbps, while an 800G optical link costs ~$2 per Gbps.
• Reduced Maintenance: Fiber optics are more durable than copper, with a lifespan of 25+ years (compared to 10–15 years for copper). This reduces replacement costs and downtime.
• Future-Proofing: Fiber can support higher bandwidths (e.g., 1.6T, 3.2T) with simple upgrades to transceivers and switches—eliminating the need to rewire data centers every few years.

For hyperscalers like Google and Meta, which operate millions of network ports, these cost savings add up to billions of dollars over a decade. For enterprises, optical networking enables them to compete in the AI era without making unsustainable capital investments.

3. Key Technological Breakthroughs Reshaping Optical Networking

The AI revolution has accelerated innovation in optical networking, leading to breakthroughs that address the unique demands of AI workloads. In this section, we explore three game-changing technologies: optical circuit switching (OCS), high-speed Ethernet (400G/800G/1.6T), and standardized interoperability (400ZR, OpenZR+).

3.1 Optical Circuit Switching (OCS): Google’s Apollo and the Future of Data Center Connectivity

Traditional data center networks rely on electronic packet switches (EPS) arranged in a “Clos” topology (also known as a spine-leaf architecture). In this setup, server racks (leaf switches) are connected to a central layer of spine switches, which route packets between leaves. While this architecture works well for general-purpose computing, it is poorly suited for AI workloads due to its high power consumption and latency.

EPS switches process each packet individually—inspecting headers, applying routing rules, and forwarding packets to their destination. This packet-level processing consumes massive amounts of power: a single 100G EPS switch can use 500+ watts, and a large data center with 1,000 switches can use 500+ kilowatts of power—equivalent to the energy used by 500 American households. Additionally, packet processing adds microseconds of latency per hop, which can slow down distributed AI training.

Optical circuit switching (OCS) addresses these limitations by replacing electronic spine switches with optical switches that establish direct light paths between leaf switches. Instead of processing individual packets, OCS switches create a dedicated “circuit” between two points, enabling data to flow directly without packet-level inspection. This results in:

• Lower Power Consumption: OCS switches use 50–70% less power than EPS switches, as they eliminate the need for packet processing hardware (e.g., ASICs, DSPs).
• Lower Latency: OCS switches enable “cut-through” routing with latency as low as 100 nanoseconds per hop—10–100x lower than EPS.
• Higher Bandwidth: OCS switches support terabit-scale circuits, making them ideal for GPU-to-GPU communication in AI clusters.

3.1.1 Google’s Apollo: The First Large-Scale OCS Deployment

Google has been at the forefront of OCS innovation with its Apollo platform—the first large-scale deployment of OCS in data center networks. Launched in 2014, Apollo has become the backbone of all Google data center networks, supporting use cases ranging from AI training to search indexing.

In a recent blog post, Google explained how Apollo differs from traditional spine-leaf architectures:

“Traditional networks use a ‘Clos’ topology (also known as a spine-leaf configuration) to connect all servers and racks within a data center. In a spine-leaf architecture, compute resources (server racks equipped with CPUs, GPUs, FPGAs, storage, and/or ASICs) are connected to leaf or top-of-rack switches, which are then connected to spines through various aggregation layers. Historically, the spines of this network use electronic packet switches (EPS)—standard network switches offered by companies like Broadcom, Cisco, Marvell, and Nvidia. However, these EPS consume significant amounts of power.”

“Apollo is believed to be the first large-scale deployment of optical circuit switching (OCS) for data center networking. The Apollo OCS platform includes custom-developed OCS, circulators, and tailored wavelength-division multiplexing (WDM) optical transceiver technology that supports bidirectional links enabled by OCS and circulators. Apollo has become the backbone of all Google data center networks, has been in production for nearly a decade, and supports all data center use cases.”

Google’s decision to adopt OCS was driven by two key factors: cost and power savings. By replacing electronic spine switches with OCS, Google eliminated the need for expensive photoelectric-optical conversions (which convert light signals to electronic signals and back) and reduced power consumption in its data center networks by 30%. Additionally, Apollo’s direct-connect architecture—where leaf switches are connected via patch panels—simplifies network management and reduces downtime.

3.1.2 The Benefits of OCS for AI Clusters

While OCS was initially deployed in data center backbones, its benefits are particularly well-suited for AI clusters. Boujelbene notes: “OCS switches have been deployed in Google’s backbone layer, but with the emergence of AI applications, we are seeing them deployed more within AI clusters because of the benefits they bring.”

For AI clusters, OCS offers three key advantages:

• High Bandwidth for GPU-to-GPU Communication: AI training requires GPUs to exchange data at terabit-scale rates. OCS switches support 400G/800G circuits, enabling seamless communication between thousands of GPUs.
• Low Latency for Real-Time Synchronization: OCS’s cut-through routing reduces latency, ensuring that GPUs can synchronize their work in real time—critical for reducing training times.
• Energy Efficiency for Large Clusters: AI clusters with 10,000+ GPUs require thousands of network links. OCS’s low power consumption reduces the cluster’s overall energy footprint.

3.1.3 The Challenges of OCS Adoption

Despite its benefits, OCS is still an emerging technology with several challenges:

• Limited Deployment: To date, only Google has successfully deployed OCS at scale. Other hyperscalers (Amazon, Microsoft) are testing OCS but have not yet rolled it out widely.
• Infrastructure Changes: OCS requires changes to existing fiber infrastructure, such as the deployment of WDM transceivers and circulators. This can be costly for organizations with legacy networks.
• Lack of Standards: OCS lacks industry-wide standards for interoperability, making it difficult for organizations to mix and match OCS components from different vendors.

However, these challenges are likely to diminish as AI-driven demand grows. Boujelbene predicts: “As AI clusters continue to expand, we will see more hyperscalers adopt OCS to address power and bandwidth challenges. Over the next five years, OCS will become a standard component of AI cluster networks.”

3.2 High-Speed Ethernet: From 400G to 1.6T and Beyond

The demand for higher bandwidth has driven the adoption of high-speed Ethernet standards—from 400G to 800G and, most recently, 1.6T. These standards are critical for AI, as they enable terabit-scale data rates between data centers, clusters, and devices.

3.2.1 400G Ethernet: The Foundation of Modern AI Networks

400G Ethernet (400GbE) was standardized in 2017 and has since become the workhorse of modern data center networks. It supports data rates of 400 gigabits per second (Gbps) over fiber optics, using technologies like quad small form-factor pluggable double density (QSFP-DD) and octal small form-factor pluggable (OSFP) transceivers.

400G’s success is due to its balance of bandwidth, power efficiency, and cost. For hyperscalers, 400G links are used for DCI, spine-leaf connections, and GPU-to-switch communication. For enterprises, 400G enables them to support AI workloads without replacing their entire network infrastructure.

One of the key drivers of 400G adoption is the 400ZR standard—a coherent pluggable optical module standard for 400G DCI applications. According to Cisco’s Acacia website: “At the 400G Ethernet level, 400ZR has been a huge success for the coherent pluggable industry, with multiple vendors and significant deployments of 400ZR QSFP-DD and OSFP modules in metro DCI applications.”

400ZR modules use coherent optical technology, which enables long-distance transmission (up to 1,000km) without signal degradation. This makes them ideal for connecting distributed data centers—critical for hyperscalers running global AI operations. IDC recently reported: “Network-grade pluggable optics such as 400ZR will see significant deployment growth in communication service provider networks in 2024.”

3.2.2 800G Ethernet: The Current AI Workhorse

As AI clusters grew larger, 400G became insufficient for some use cases. Enter 800G Ethernet (800GbE), which was standardized in 2021 and has since become the preferred choice for high-performance AI networks.

800G offers twice the bandwidth of 400G, with power consumption of ~15–20 watts per transceiver—only slightly higher than 400G. This makes it ideal for:

• GPU-to-Switch Communication: Modern GPUs (e.g., Nvidia H100) support 800G links, enabling terabit-scale data rates between GPUs and switches.
• DCI for Large AI Clusters: Hyperscalers use 800G links to connect data centers with massive AI clusters, ensuring that data can be transferred quickly and efficiently.
• Edge-to-Cloud Connectivity: 800G enables edge AI devices (e.g., autonomous vehicles, smart city sensors) to transmit large datasets to cloud data centers.

800G adoption is growing rapidly. According to Dell’Oro Group, 800G port shipments are expected to grow at a compound annual growth rate (CAGR) of 120% between 2023 and 2027. By 2025, most ports in AI networks will be 800G, and by 2027, most will be 1.6T.

3.2.3 1.6T Ethernet: The Next Frontier for AI

As AI clusters continue to expand (some now have 32K+ GPUs), 800G is already becoming insufficient. This has driven the development of 1.6T Ethernet (1.6TbE), which supports data rates of 1.6 terabits per second—four times the bandwidth of 400G.

1.6T Ethernet uses advanced technologies like dense wavelength-division multiplexing (DWDM) and high-order modulation (e.g., 64QAM) to achieve terabit-scale data rates. It also uses new transceiver form factors, such as the 400G small form-factor pluggable (SFP-DD) and the 800G OSFP, to support higher density.

At the 2023 Fiber Optic Communication Conference (OFC), vendors like Cisco, Broadcom, and Nvidia demonstrated 1.6T optical components and transceivers. At OFC 2024, many of these products were showcased as production-ready. Boujelbene writes: “While we expect 1.6T shipments to not materialize until 2025/2026, the industry must already start working on 3.2T and explore the various paths and options to reach this milestone.”

The urgency for 1.6T stems from the exponential growth of AI cluster bandwidth demands. Boujelbene explains: “This urgency stems from multiple factors, including the sharp growth in bandwidth requirements within AI clusters, as well as the escalating power and cost concerns associated with higher speeds.”

3.3 Standardization: 400ZR, OpenZR+, and the Race for Interoperability

One of the biggest challenges in optical networking is interoperability. With multiple vendors offering optical components (transceivers, switches, WDM systems), ensuring that these components work together is critical for enterprises and hyperscalers. This has driven the development of industry standards like 400ZR and OpenZR+.

3.3.1 400ZR: Coherent Pluggable for Metro DCI

400ZR is a standard for coherent pluggable optical modules, developed by the Optical Internetworking Forum (OIF). It was standardized in 2019 and is designed for metro DCI applications (distances up to 1,000km). 400ZR modules use QSFP-DD and OSFP form factors, making them compatible with existing network equipment.
The key benefit of 400ZR is its interoperability. Multiple vendors (Cisco, Acacia, Inphi, II-VI) offer 400ZR modules, enabling organizations to mix and match components without compatibility issues. This has driven widespread adoption in metro DCI networks, where hyperscalers and service providers need to connect data centers quickly and cost-effectively.

3.3.2 OpenZR+: Extending Interoperability to Long-Haul DCI

While 400ZR is ideal for metro DCI, it is limited to distances of up to 1,000km. For long-haul DCI (distances up to 3,000km), the industry has developed OpenZR+—an initiative to extend the 400ZR standard to longer distances.
OpenZR+ is led by a consortium of vendors, including Cisco, Acacia, and Inphi. It builds on 400ZR’s coherent technology but adds features like forward error correction (FEC) and higher-order modulation to support longer distances. OpenZR+ modules are expected to be standardized in 2024 and will enable hyperscalers to connect data centers across continents—critical for global AI model training.

3.3.3 The Importance of Standardization for AI

Standardization is particularly important for AI networks, which require large-scale deployment of optical components. Without standards, organizations would be locked into a single vendor, increasing costs and limiting flexibility. Standards like 400ZR and OpenZR+ enable:

• Vendor Neutrality: Organizations can choose components from multiple vendors, ensuring competitive pricing and access to the latest technology.
• Scalability: Standards enable organizations to scale their networks by adding components from different vendors, without worrying about compatibility.
• Reduced Risk: Standards reduce the risk of vendor lock-in and ensure that components will be supported for years to come.

As Gartner notes: “With AI data centers becoming more distributed, effectively connecting geographically dispersed data centers via DCI will be a key driver for AI and optical networks. These links’ capacity needs to grow as AI applications increase.” Standards like 400ZR and OpenZR+ will be critical to meeting this demand.

4. The AI Cluster Explosion: Bandwidth Demands at Exponential Scale

The size and complexity of AI clusters are growing at an unprecedented rate, driven by the need to train larger and more powerful AI models. This growth is placing enormous pressure on optical networks, as clusters require terabit-scale bandwidth to support GPU-to-GPU communication, data storage, and distributed processing.

4.1 The Exponential Growth of AI Clusters

AI clusters are groups of GPUs (or other accelerators) working in parallel to train or run AI models. Over the past decade, the size of these clusters has grown exponentially:

• 2015: Typical AI clusters had 16–32 GPUs, supporting models with millions of parameters.
• 2020: Clusters grew to 256–1,000 GPUs, supporting models with billions of parameters (e.g., GPT-3).
• 2024: Clusters now have 4K–64K GPUs, supporting models with trillions of parameters (e.g., GPT-4, PaLM 2).

Boujelbene explains: “The size of emerging AI applications appears to be growing exponentially, with the number of parameters these applications need to process increasing 1,000x every 2 to 3 years. As a result, AI cluster sizes (measured by the number of accelerators) are quadrupling every 2 years, from a typical 256 to 1,000, then quickly to 4K, and now some clusters have 32K and 64K accelerators.”

This growth is driven by two factors:

• Model Size: Larger models (with more parameters) deliver better performance but require more compute power and data. For example, GPT-4 has an estimated 1.76 trillion parameters—10x more than GPT-3.
• Parallel Processing: Training large models requires parallel processing across thousands of GPUs. Each GPU processes a portion of the data, and results are aggregated to update the model.

4.2 Bandwidth Requirements for AI Clusters

The growth of AI clusters has created a corresponding demand for bandwidth. To understand this demand, consider the following:

• Inter-GPU Bandwidth: Each GPU in a cluster needs to communicate with other GPUs to share intermediate results. For a cluster with 10,000 GPUs, each GPU requires a bandwidth of ~100 Gbps to other GPUs, resulting in a total cluster bandwidth of ~1 Pbps (1,000 Tbps).
• Storage Bandwidth: AI clusters require fast access to petabytes of training data. A cluster with 10,000 GPUs needs a storage bandwidth of ~1 Tbps to feed data to the GPUs.
• DCI Bandwidth: Distributed clusters require terabit-scale DCI links to connect data centers. For example, a cluster spread across two data centers needs 800G+ links to transfer data between locations.

These bandwidth requirements are pushing optical networking to its limits. Traditional 100G/400G links are insufficient for modern AI clusters, driving the adoption of 800G/1.6T Ethernet and OCS.

4.3 Case Study: Meta’s AI Cluster

Meta’s AI cluster, known as the “AI Research SuperCluster (RSC),” is a prime example of the bandwidth demands of modern AI. Launched in 2022, the RSC consists of 24,000 Nvidia A100 GPUs connected by 800G optical links.

The RSC requires a total inter-GPU bandwidth of ~2.4 Pbps (24,000 GPUs × 100 Gbps per GPU). To meet this demand, Meta deployed 800G Ethernet switches and optical transceivers, enabling terabit-scale communication between GPUs.

Meta’s RSC is used to train large language models (e.g., Llama 2) and computer vision models. The cluster’s optical network ensures that training times are minimized, allowing Meta to iterate on models quickly.

4.4 The Future of AI Clusters: 100K+ GPUs

The growth of AI clusters shows no signs of slowing down. Boujelbene predicts that by 2027, some AI clusters will have 100K+ GPUs, requiring total bandwidth of ~10 Pbps. This will drive the adoption of 3.2T Ethernet and advanced OCS technologies.

To support these clusters, optical networking will need to evolve in three key ways:

• Higher Bandwidth: 3.2T Ethernet will become the standard for inter-GPU and DCI links.
• Lower Latency: Advanced OCS and coherent optical technologies will reduce latency to sub-100 nanoseconds per hop.
• Higher Density: Optical components will become smaller and more dense, enabling thousands of links to be deployed in a single rack.

5. The Great Optical Debate: LPO vs. CPO—Which Will Dominate AI Networks?

As AI networks push toward 800G/1.6T and beyond, two technologies have emerged as front-runners for enabling terabit-scale connectivity: Linear Drive Pluggable (LPO) and Co-Packaged Optics (CPO). Both technologies aim to reduce power consumption and improve bandwidth density, but they take different approaches. In this section, we compare LPO and CPO, explore their tradeoffs, and predict their adoption timelines.

5.1 Linear Drive Pluggable (LPO): Simplifying Deployment with Lower Power

Linear Drive Pluggable (LPO) is an optical technology that eliminates the digital signal processor (DSP) from pluggable transceivers. Traditional optical transceivers use DSPs to compensate for signal degradation (e.g., dispersion, noise), but DSPs are power-hungry and expensive. LPO replaces DSPs with a “linear drive” circuit that directly modulates the laser, reducing power consumption by 30–50%.

5.1.1 How LPO Works

LPO transceivers work by:

• Eliminating the DSP: Instead of using a DSP to process the signal, LPO uses a linear drive circuit to modulate the laser directly.
• Leveraging Forward Error Correction (FEC): FEC is moved from the transceiver to the host ASIC (e.g., switch ASIC, GPU), reducing the transceiver’s complexity.
• Using High-Speed SerDes: LPO uses high-speed serializer/deserializer (SerDes) on the host ASIC to interface with the transceiver, enabling terabit-scale data rates.

The result is a transceiver that is smaller, cheaper, and more energy-efficient than traditional DSP-based transceivers. For example, an 800G LPO transceiver uses ~12 watts of power, compared to 18–20 watts for a traditional 800G transceiver.

5.1.2 The Rise of LPO MSA

To accelerate LPO adoption, 12 leading optical vendors—including Cisco, Broadcom, Intel, Nvidia, Arista, and AMD—formed the Linear Drive Pluggable Optical Multi-Source Agreement (LPO MSA) in March 2024. The LPO MSA aims to develop a common standard for LPO transceivers, ensuring interoperability between vendors.

Mark Nowell, Chairman of the LPO MSA, stated: “There is an urgent need to reduce network power consumption for AI and other high-performance applications. LPO significantly reduces module and system power while maintaining a pluggable interface, providing customers with the economics and flexibility needed for high-volume deployments.”

The LPO MSA is focusing on developing standards for 800G and 1.6T LPO transceivers, with the first products expected to ship in 2025.

5.1.3 Use Cases for LPO

LPO is ideal for:

• AI Clusters: LPO’s low power consumption and pluggable form factor make it ideal for GPU-to-switch and switch-to-switch links in AI clusters.
• Data Center Backbones: LPO transceivers can be used in spine-leaf architectures to reduce power consumption.
• Edge AI: LPO’s small size and low power make it suitable for edge AI devices (e.g., autonomous vehicles, smart city sensors) that have limited power and space.

5.2 Co-Packaged Optics (CPO): Integration for Extreme Density

Co-Packaged Optics (CPO) is an optical technology that integrates optical components (transceivers, WDM systems) directly onto the same package as the switch ASIC or GPU. This eliminates the need for separate pluggable transceivers and reduces the distance between the ASIC and optical components, resulting in lower latency and higher density.

5.2.1 How CPO Works

CPO works by:

• Integrating Optical Components: Optical transceivers and WDM systems are integrated onto the same package as the switch ASIC or GPU.
• Reducing Signal Path Length: The distance between the ASIC and optical components is reduced from inches (for pluggable transceivers) to millimeters, reducing signal degradation and latency.
• Increasing Density: CPO enables hundreds of optical links to be integrated into a single package, increasing bandwidth density by 2–3x compared to pluggable transceivers.

The result is a system that is more energy-efficient, lower-latency, and higher-density than traditional pluggable transceivers. For example, a CPO-enabled switch ASIC can support 16Tbps of bandwidth with a power consumption of ~200 watts, compared to 400+ watts for a traditional switch with pluggable transceivers.

5.2.2 CPO’s Advantages for AI

CPO offers several advantages for AI networks:

• Extreme Density: CPO enables thousands of optical links to be deployed in a single rack, supporting large AI clusters with 10,000+ GPUs.
• Ultra-Low Latency: CPO’s short signal path reduces latency to sub-10 nanoseconds per hop, critical for real-time GPU-to-GPU communication.
• High Energy Efficiency: CPO’s integration reduces power consumption by 40–60% compared to traditional pluggable transceivers.

5.2.3 Challenges for CPO

Despite its advantages, CPO faces several challenges:

• Complexity: CPO requires tight integration between the ASIC and optical components, making it more complex to design and manufacture than pluggable transceivers.
• Cost: CPO packages are more expensive than pluggable transceivers, making them less suitable for small-scale deployments.
• Interoperability: CPO lacks industry standards, making it difficult for organizations to mix and match components from different vendors.

5.3 A Comparative Analysis: LPO vs. CPO

To help organizations choose between LPO and CPO, we compare the two technologies across key metrics:

5.3.1 Which Will Dominate?

The answer depends on the use case:

• Short-Term (2025–2026): LPO will dominate mid-sized AI clusters (1K–4K GPUs) and enterprise networks. Its low cost, simplicity, and interoperability make it the ideal choice for organizations looking to scale their AI operations without significant infrastructure changes.
• Long-Term (2027+): CPO will dominate large-scale AI clusters (32K+ GPUs) and hyperscaler backbones. Its extreme density and ultra-low latency make it the only viable option for supporting the next generation of AI models.

Boujelbene agrees: “LPO seems to be ahead of CPO in meeting these requirements [multi-vendor support, time-to-market, maintainability] because it retains the pluggable form factor (only removing the DSP). Therefore, we expect LPO to achieve volume deployment before CPO.”

6. Industry Challenges: Energy, Cost, and Infrastructure Upgrades

While optical networking offers significant benefits for AI, the industry faces several challenges that must be addressed to enable widespread adoption. These challenges include energy consumption, cost scalability, and infrastructure upgrades.

6.1 Energy Consumption: The Hidden Cost of AI Networking

AI is already a major consumer of energy, and networking is a significant contributor. According to a 2023 study by Stanford University, data center networks consume 10–15% of the total energy used by data centers. For hyperscalers running large AI clusters, this translates to billions of dollars in annual energy costs.

Optical networking reduces energy consumption compared to traditional solutions, but it is not immune to the challenges of higher speeds. As data rates increase from 400G to 800G to 1.6T, the power consumption of optical components (transceivers, switches) increases—albeit at a slower rate than electronic alternatives.

To address this challenge, the industry is focusing on three strategies:

• Low-Power Components: Vendors are developing low-power transceivers (e.g., LPO) and switches (e.g., OCS) that reduce power consumption by 30–60%.
• Energy-Efficient Architectures: Hyperscalers are adopting architectures like OCS and CPO that reduce power consumption by eliminating redundant components (e.g., DSPs, electronic switches).
• Renewable Energy: Organizations are powering their data centers with renewable energy (solar, wind) to reduce their carbon footprint.

6.2 Cost Scalability: Balancing Performance and Budget

Optical networking’s upfront cost is higher than traditional networking, making it a barrier for small and medium-sized enterprises (SMEs). For example:

• An 800G optical transceiver costs ~$2,000–$3,000, compared to $500–$1,000 for a 100G copper transceiver.
• An OCS switch costs ~$100,000–$200,000, compared to $50,000–$100,000 for an electronic packet switch.

For hyperscalers with deep pockets, these costs are justified by the performance benefits. But for SMEs, they can be prohibitive. To address this challenge, the industry is focusing on:

• Cost Reduction Through Scale: As demand for optical components grows, vendors are able to reduce costs through economies of scale. For example, the cost of 400G transceivers has dropped by 50% since 2020.
• Standardization: Standards like 400ZR and LPO MSA are increasing competition, driving down prices.
• Tiered Solutions: Vendors are offering tiered solutions for SMEs, such as lower-cost 400G transceivers and smaller OCS switches.

6.3 Infrastructure Upgrades: Rewiring for the AI Era

Many organizations have legacy fiber infrastructure that is not designed for terabit-scale data rates. For example, older fiber cables may not support DWDM technology, and existing patch panels may not accommodate new transceiver form factors (e.g., QSFP-DD, OSFP).

Upgrading this infrastructure is costly and time-consuming. For example:

• Rewiring a data center with new fiber cables can cost $100–$200 per square foot.
• Upgrading patch panels and switches can take months, leading to downtime.

To address this challenge, organizations are adopting two strategies:

• Incremental Upgrades: Instead of replacing their entire infrastructure at once, organizations are upgrading incrementally—starting with AI clusters and DCI links.
• Future-Proofing: Organizations are investing in fiber infrastructure that supports 1.6T/3.2T data rates, ensuring that they do not need to rewire again in the near future.

6.4 Skills Gap: Training the Next Generation of Optical Engineers

Optical networking is a complex technology that requires specialized skills. However, there is a growing skills gap in the industry, as many engineers lack experience with terabit-scale optical systems, coherent technology, and AI-optimized networks.

To address this challenge, vendors and educational institutions are partnering to:

• Offer Training Programs: Vendors like Cisco and Nvidia offer training programs for optical networking, covering topics like 800G Ethernet, OCS, and CPO.
• Develop Certification: Organizations like the Optical Internetworking Forum (OIF) offer certifications for optical engineers, validating their skills in terabit-scale networking.
• Collaborate with Universities: Vendors are partnering with universities to develop curricula for optical networking, ensuring that new engineers have the skills needed for the AI era.

7. Vendor Strategies: How Cisco, Nvidia, Broadcom, and Others Are Leading the Charge

The AI-driven demand for optical networking has sparked a race among vendors to deliver the fastest, most energy-efficient, and most scalable solutions. In this section, we explore the strategies of leading vendors—Cisco, Nvidia, Broadcom, Google, and others—and how they are shaping the future of optical networking.

7.1 Cisco: The End-to-End Optical Leader

Cisco is a dominant player in optical networking, with a comprehensive portfolio of products ranging from transceivers to switches to WDM systems. Cisco’s strategy is centered on three key pillars:

• End-to-End Solutions: Cisco offers end-to-end optical solutions for AI networks, including 800G/1.6T transceivers (Acacia), OCS switches, and WDM systems. This allows organizations to deploy a single-vendor solution, reducing complexity and improving support.
• Acacia Acquisition: In 2021, Cisco acquired Acacia Communications—a leading provider of coherent optical components—for $45 billion. This acquisition gave Cisco access to Acacia’s advanced coherent technology, which is critical for 400ZR/OpenZR+ and 1.6T transceivers.
• LPO MSA Leadership: Cisco is a founding member of the LPO MSA and is leading the development of LPO standards for 800G/1.6T transceivers.

Cisco’s focus on end-to-end solutions has made it a top choice for hyperscalers and enterprises. For example, Google uses Cisco’s Acacia transceivers in its Apollo OCS platform, and Meta uses Cisco’s 800G switches in its AI clusters.

7.2 Nvidia: The AI-Optimized Optical Leader

Nvidia is best known for its GPUs, but the company has become a major player in optical networking through its Mellanox acquisition (2020) and its focus on AI-optimized networks. Nvidia’s strategy is centered on:

• GPU-Optical Integration: Nvidia integrates optical components into its GPU platforms (e.g., H100) to enable terabit-scale communication between GPUs. For example, the H100 supports 800G links via Nvidia’s Quantum-2 switch.
• InfiniBand: Nvidia’s InfiniBand technology—originally designed for high-performance computing (HPC)—is now used in AI clusters for low-latency, high-bandwidth communication. InfiniBand supports 400G/800G links and is optimized for GPU-to-GPU communication.
• LPO and CPO Development: Nvidia is a founding member of the LPO MSA and is developing CPO solutions for its next-generation GPUs.

Nvidia’s focus on GPU-optical integration has made it a top choice for AI clusters. For example, OpenAI uses Nvidia’s H100 GPUs and InfiniBand switches in its ChatGPT training clusters.

7.3 Broadcom: The Component Leader

Broadcom is a leading provider of optical components, including transceivers, switches, and ASICs. Broadcom’s strategy is centered on:

• High-Speed Components: Broadcom offers a comprehensive portfolio of 400G/800G/1.6T transceivers and switches, including its Tomahawk 5 switch ASIC (which supports 16Tbps of bandwidth).
• LPO Leadership: Broadcom is a founding member of the LPO MSA and is developing LPO transceivers for 800G/1.6T data rates.
• CPO Development: Broadcom is developing CPO solutions for its next-generation switch ASICs, enabling extreme density for AI clusters.

Broadcom’s components are used by nearly all major network vendors, including Cisco, Arista, and Nvidia. For example, Broadcom’s Tomahawk 5 ASIC is used in Arista’s 7800R3 switch, which is deployed in Meta’s AI clusters.

7.4 Google: The OCS Innovator

Google is not a traditional optical vendor, but its Apollo OCS platform has reshaped the industry. Google’s strategy is centered on:

• Custom Optical Components: Google develops custom OCS switches, circulators, and WDM transceivers for its Apollo platform, enabling it to optimize for power consumption and latency.
• Open Source: Google has open-sourced some of its optical technologies, including its WDM transceiver designs, to encourage industry adoption.
• AI-Optimized Networks: Google’s optical networks are optimized for AI workloads, with OCS switches deployed in AI clusters to support GPU-to-GPU communication.

Google’s Apollo platform has set a benchmark for OCS deployment, and other hyperscalers are following its lead. For example, Amazon is testing OCS switches in its data center networks.

7.5 Other Key Vendors

• Arista Networks: Arista is a leading provider of data center switches, including its 7800R3 switch (which supports 800G/1.6T links). Arista is a founding member of the LPO MSA and is developing AI-optimized switches for large clusters.
• II-VI Incorporated: II-VI is a leading provider of optical components, including coherent transceivers and WDM systems. II-VI’s components are used in 400ZR/OpenZR+ deployments and are optimized for DCI applications.
• Inphi Corporation: Inphi (acquired by Marvell in 2021) is a leading provider of coherent transceivers and DSPs. Inphi’s components are used in 400G/800G transceivers and are optimized for low power consumption.

8. Future Trends: 3.2T Optics, AI-Optimized Optical Networks, and the Next Frontier

The future of optical networking is shaped by the evolving demands of AI. In this section, we predict three key trends that will define the industry over the next five years: the rise of 3.2T optics, the development of AI-optimized optical networks, and the integration of optical networking with emerging technologies (e.g., quantum computing, 6G).

8.1 3.2T Optics: The Next Step in Bandwidth Scaling

As AI clusters grow to 100K+ GPUs, 1.6T optics will become insufficient. This will drive the development of 3.2T Ethernet (3.2TbE), which supports data rates of 3.2 terabits per second—twice the bandwidth of 1.6T.

8.1.1 How 3.2T Will Work

3.2T optics will build on the technologies used in 1.6T, including:

• Advanced Modulation: 3.2T transceivers will use higher-order modulation (e.g., 128QAM) to pack more data into each wavelength.
• Dense WDM: 3.2T transceivers will use DWDM to support multiple wavelengths per fiber strand, enabling 3.2T per wavelength.
• Low-Power Components: 3.2T transceivers will use LPO or CPO technology to reduce power consumption, ensuring that they are energy-efficient.

8.1.2 Adoption Timeline

The industry is already beginning to work on 3.2T optics. Boujelbene writes: “While we expect 1.6T shipments to not materialize until 2025/2026, the industry must already start working on 3.2T and explore the various paths and options to reach this milestone.”

We predict that:

• 2024–2025: Vendors will demonstrate 3.2T prototypes at industry conferences (e.g., OFC 2025).
• 2026–2027: 3.2T transceivers will be standardized and begin shipping in small volumes.
• 2028–2030: 3.2T will become the standard for large AI clusters and DCI links.

8.2 AI-Optimized Optical Networks: Self-Driving Networks

The complexity of AI networks will drive the development of “self-driving” optical networks—networks that use AI to optimize performance, reduce latency, and minimize power consumption.

8.2.1 Key Features of AI-Optimized Optical Networks

AI-optimized optical networks will include:

• Dynamic Bandwidth Allocation: AI will monitor AI workloads and allocate bandwidth dynamically, ensuring that GPUs and storage systems have the bandwidth they need when they need it.
• Predictive Maintenance: AI will analyze network data to predict failures (e.g., transceiver degradation, fiber damage) and schedule maintenance before downtime occurs.
• Latency Optimization: AI will optimize routing paths to minimize latency, ensuring that real-time AI operations (e.g., inference) are not delayed.
• Energy Management: AI will adjust power consumption based on workload demands, reducing energy use during low-traffic periods.

8.2.2 Vendor Initiatives

Vendors are already investing in AI-optimized optical networks:

• Cisco: Cisco’s Crosswork Network Automation platform uses AI to optimize optical networks, including dynamic bandwidth allocation and predictive maintenance.
• Nvidia: Nvidia’s Cumulus Linux uses AI to manage network traffic in AI clusters, ensuring that bandwidth is allocated to the most critical workloads.
• Google: Google’s Apollo platform uses AI to optimize OCS circuit routing, reducing latency and power consumption.

8.3 Integration with Emerging Technologies

Optical networking will play a critical role in enabling emerging technologies that will shape the next decade, including quantum computing, 6G, and edge AI.

8.3.1 Quantum Computing

Quantum computing requires ultra-low latency and high bandwidth to connect quantum processors (qubits) and classical computers. Optical networking is ideal for this, as it offers latency as low as 10 nanoseconds per hop and terabit-scale bandwidth.
Vendors like Cisco and II-VI are already developing optical components for quantum computing, including low-loss fiber cables and ultra-low-latency transceivers.

8.3.2 6G

6G—the next generation of wireless technology—will require terabit-scale backhaul links to support applications like autonomous vehicles, holographic communication, and immersive VR/AR. Optical networking will be the backbone of 6G backhaul, enabling terabit-scale data rates between cell towers and data centers.

8.3.3 Edge AI

Edge AI applications (e.g., smart cities, autonomous vehicles) generate massive amounts of data that must be processed locally and transmitted to cloud data centers. Optical networking will enable edge-to-cloud connectivity at terabit-scale rates, ensuring that edge AI applications can operate in real time.

9. Conclusion: Optical Networking as the Backbone of AI’s Next Chapter

The AI revolution has transformed optical networking from a supporting technology to the backbone of modern computing. As AI workloads grow larger, more distributed, and more demanding, optical networking is the only technology capable of delivering the bandwidth, latency, and energy efficiency required to power them.

From Google’s Apollo OCS platform to 800G/1.6T Ethernet to the battle between LPO and CPO, the optical networking industry is evolving at an unprecedented pace. Vendors are racing to deliver solutions that address the unique challenges of AI, while enterprises and hyperscalers are investing in optical infrastructure to stay ahead of the curve.

The future of optical networking is bright. Over the next five years, we will see the rise of 3.2T optics, AI-optimized self-driving networks, and integration with emerging technologies like quantum computing and 6G. These advancements will not only enable the next generation of AI applications but will also reshape the way we think about networking—from a static infrastructure to a dynamic, intelligent system that adapts to the needs of AI.

As Bill Gartner notes: “Fiber is the only connectivity technology capable of delivering the capacity organizations need over the distances required.”

For AI to reach its full potential, optical networking will remain its most critical enabler. The organizations that invest in optical infrastructure today will be the leaders of the AI era tomorrow.