AI Interconnect Market

The AI interconnect market reached an estimated USD 3,620 million in 2025 and is projected to climb to USD 31,850 million by 2032, expanding at a CAGR of 37% from 2026 to 2032. This growth is driven by a networking bottleneck that scales with every GPU added: a 100,000-GPU training cluster is useless if the GPUs cannot communicate fast enough to synchronize their work. The data center Ethernet switch market reached USD 15.4 billion in Q1 2026, up 39.8% year-over-year, with NVIDIA's share of that market surging from under 4% to 21.5% in a single year—a share gain unmatched in networking history. Arista holds 18.9% of data center Ethernet share with Q2 2025 revenue of USD 2.2 billion, growing 30.4% year-over-year. Cisco received over USD 2 billion in AI infrastructure orders in FY2025, more than doubling its original USD 1 billion target. The majority of switch port shipments in AI clusters during 2025 operate at 800 gigabits per second; by 2027, the majority will move to 1.6 terabits; by 2030, to 3.2 terabits—implying that the network electrical layer requires full replacement at each bandwidth generation, an upgrade cadence far more aggressive than any prior era of enterprise networking. NVIDIA's Quantum-X800 InfiniBand delivers 144 ports at 800 Gb/s with SHARP v4 in-network computing. NVLink 5, announced at Computex 2025, delivers 1.8 TB/s per GPU across up to 576 GPUs. The Ultra Ethernet Consortium finalized its UEC 1.0 specification in June 2025, closing most of the latency gap between Ethernet and InfiniBand and backed by AMD, Arista, Broadcom, Cisco, Intel, Meta, and Microsoft. The AI interconnect market is where the physics of GPU communication meets the economics of bandwidth—and it is growing as the defining infrastructure constraint of the AI era.

Top 10 Key Takeaways

• North America is the largest regional market, concentrating hyperscaler training clusters and the headquarters of NVIDIA, Broadcom, Arista, Cisco, and Marvell.
• InfiniBand switches and adapters lead by revenue in AI back-end training networks; AI-optimized Ethernet is the fastest-growing as UEC 1.0 closes the performance gap.
• Hyperscale AI training clusters (10,000–100,000+ GPUs) are the leading deployment context; enterprise AI infrastructure is the fastest-growing as mid-market organizations build GPU clusters.
• 800G is the 2025 deployment standard; 1.6T arrives in 2027; 3.2T by 2030—each generation requiring full network refresh.
• The InfiniBand vs. Ethernet contest is the defining rivalry: NVIDIA's proprietary InfiniBand offers the lowest latency; UEC 1.0 Ethernet offers multi-vendor openness and competitive performance.
• NVIDIA's data center Ethernet switch share jumped from under 4% to 21.5% in one year—selling both the GPU and the network that connects GPUs is a singular bundling advantage.
• NVLink 5 (1.8 TB/s per GPU, 576-GPU domains) redefines intra-node interconnect; Scale-Across extends GPU connectivity across multiple data centers.
• The near-term opportunity lies in 1.6T optical transceivers, co-packaged optics for AI bandwidth at reduced power, and UEC 1.0 Ethernet deployment replacing InfiniBand in new clusters.
• The near-term risk is power consumption (800G modules at 14–20W per port), laser supply bottlenecks for optical module production, and the capital burden of replacing the entire network fabric every two to three years.

Why the AI Interconnect Market Matters Now

A GPU cannot train a model alone. Distributed training splits the model, the data, and the computation across thousands of GPUs, and every GPU must exchange gradient updates, activation tensors, and synchronization signals with every other GPU at speeds measured in terabits per second. The interconnect—the switches, adapters, cables, optical transceivers, and silicon that carry this traffic—determines whether a 10,000-GPU cluster trains at its theoretical peak or spends half its time waiting for data to traverse the network.

The market covers the networking hardware, optical components, switch silicon, and connectivity platforms that enable GPU-to-GPU, node-to-node, rack-to-rack, and data-center-to-data-center communication in AI infrastructure. It includes InfiniBand switches and adapters (NVIDIA Quantum-X800, ConnectX-8), AI-optimized Ethernet switches (NVIDIA Spectrum-X, Arista 7800R4, Cisco Nexus AI, Broadcom Tomahawk 5), NVLink and NVSwitch (intra-node GPU-to-GPU), optical transceivers and active optical cables (800G, 1.6T from Coherent, Innolight, Lumentum, Eoptolink), DPUs and SmartNICs (BlueField-3, Broadcom, AMD Pensando), and switch silicon ASICs (Broadcom Tomahawk/Jericho, Cisco Silicon One, Marvell). Out of scope are the GPUs and servers themselves (covered in [INTERNAL LINK: AI chip market] and [INTERNAL LINK: AI server market]), general-purpose enterprise networking without AI-specific optimization, and WAN/internet infrastructure. The market connects to the [INTERNAL LINK: data center networking market], the [INTERNAL LINK: optical transceiver market], the [INTERNAL LINK: InfiniBand market], and the [INTERNAL LINK: AI infrastructure market].

Market Trends Shaping the AI Interconnect Market

The defining trend is the InfiniBand vs. Ethernet contest for AI networking dominance. InfiniBand, controlled by NVIDIA through its Quantum switches and ConnectX adapters, delivers approximately 1–2 microsecond latency and has been the default back-end fabric for GPU training clusters. Ethernet, backed by the Ultra Ethernet Consortium (UEC 1.0 finalized June 2025), is closing the performance gap with AI-specific enhancements to congestion control, multipath, and collective operations. The UEC coalition—AMD, Arista, Broadcom, Cisco, Intel, Meta, and Microsoft—represents the industry's shared interest in an open alternative to NVIDIA's proprietary fabric. InfiniBand surged in Q2 2025 on Blackwell Ultra demand, but Ethernet maintains the overall market lead and is gaining AI-specific share. Arista's CEO has publicly called Ethernet the "eventual winner and equalizer" for AI networking.

A second trend is NVIDIA's networking share jump from under 4% to 21.5% of data center Ethernet switches in a single year. NVIDIA is now selling both the GPU and the network that connects GPUs—a bundling advantage that no competitor can replicate. Spectrum-X, NVIDIA's AI-optimized Ethernet platform, and the Quantum-X800 InfiniBand switch are driving this share gain, and NVIDIA's annualized networking business has reached approximately USD 60 billion.

A third trend is the 800G → 1.6T → 3.2T bandwidth roadmap creating a two-to-three-year network refresh cycle. Each bandwidth generation requires new switches, new transceivers, new cabling, and new power/cooling—a full replacement of the network electrical layer that is far more aggressive than the four-to-five-year refresh cycles of traditional enterprise networking. Optical transceiver shipments above 400G are expected to exceed 31.9 million units in 2025 (56.5% YoY growth), with 1.6T demand in 2026 exceeding expectations at 11 million projected units.

A fourth trend is NVLink 5 and Scale-Across redefining interconnect architecture. NVLink 5, announced at Computex 2025, delivers 1.8 TB/s per GPU (18 connections at 100 GB/s each) with NVLink Switch enabling 130 TB/s aggregate bandwidth across up to 576 GPUs. Scale-Across, introduced by both NVIDIA and Broadcom in August 2025, extends GPU connectivity across multiple data centers, enabling training runs that span geographically distributed clusters.

A fifth trend is co-packaged optics (CPO) and silicon photonics approaching production deployment. CPO integrates optical transceivers directly onto the switch ASIC package, eliminating the pluggable module and reducing interconnect power by 50%+ while increasing bandwidth density. Broadcom is the leading CPO proponent, with production-grade designs targeting AI data center deployment. Silicon photonics, where optical components are fabricated on a silicon wafer, promises further integration and cost reduction.

Market Drivers Accelerating Growth

The first driver is GPU cluster scale exceeding network bandwidth at every generation. A 100,000-GPU cluster running all-reduce operations generates collective traffic that saturates any network not designed specifically for AI. Every GPU added to the cluster demands proportionally more network bandwidth—driving switch, adapter, and optical transceiver sales in lockstep with GPU shipments.

The second driver is the 39.8% YoY growth in data center Ethernet switching (Q1 2026). AI workloads are the primary demand driver for 800G switch ports, and the growth rate indicates that AI networking investment is accelerating faster than overall data center spending.

The third driver is the optical transceiver supercycle. Shipments of 800G+ transceivers are surging, with 1.6T modules entering volume production in 2026. Every AI cluster requires hundreds to thousands of optical modules, and the per-module value at 800G and 1.6T is substantially higher than at prior generations.

Market Challenges and Restraints

The most significant restraint is power consumption. 800G optical modules draw 14–20W per port, and a 144-port switch consumes over 2 kW in optics alone. At 1.6T, per-port power will increase further. This power draw competes with GPU power budgets within the same rack thermal envelope, creating a design constraint that limits interconnect density.

A second restraint is laser light source supply. Industry analysts identify upstream laser supply as an emerging bottleneck for 800G and 1.6T optical module production. The demand surge from hyperscaler GPU clusters is outpacing laser manufacturing capacity, and supply constraints could delay optical module availability.

A third challenge is the capital burden of two-to-three-year network refresh cycles. Replacing the entire network fabric every bandwidth generation—switches, optics, cabling, power distribution—imposes capital costs that scale with cluster size, making AI networking a recurring multi-billion-dollar investment for hyperscalers.

Segment Insights

By Component

The AI interconnect market includes switches, adapters, optical transceivers, active optical cables, NVLink/NVSwitch systems, DPUs and SmartNICs, and switch silicon ASICs. InfiniBand switches and adapters represent a major revenue component in AI back-end networks, while optical transceivers and switch silicon are becoming increasingly important as bandwidth generations accelerate.

By Deployment Mode

AI interconnect infrastructure is deployed across hyperscale data centers, enterprise AI infrastructure, specialized GPU clouds, and colocation AI facilities. Hyperscale deployments require the highest port density, bandwidth, and redundancy, while enterprise and colocation deployments are expanding as more organizations build dedicated GPU infrastructure.

By Application

AI interconnect technologies support distributed AI training, AI inference, high-performance computing, GPU clustering, model serving, and data-intensive workloads. Distributed AI training represents the most demanding application because synchronization and collective communication require extremely high bandwidth and low latency across thousands of GPUs.

By End User

Hyperscale AI training clusters (10,000–100,000+ GPUs) lead by expenditure, because they consume the most switches, adapters, and optical modules per deployment and demand the highest-bandwidth, lowest-latency fabric.

Enterprise AI infrastructure (100–5,000 GPUs) is the fastest-growing deployment context, as mid-market organizations build GPU clusters for the first time and need AI-optimized networking to support distributed training and inference.

Key segmentation conclusions:

• InfiniBand leads training network revenue; AI Ethernet grows fastest on UEC 1.0 and multi-vendor openness.
• Hyperscale clusters lead expenditure; enterprise AI infrastructure grows fastest by customer count.
• 800G is the 2025 standard; 1.6T arrives 2027; network refresh every 2–3 years is the new normal.
• NVLink 5 dominates intra-node; InfiniBand and Ethernet compete for inter-node and inter-rack.
• NVIDIA's bundling of GPU + network is a singular competitive advantage—and the primary vendor lock-in concern.

Regional Analysis: AI Interconnect Market by Region

North America

North America holds the largest base, valued at roughly USD 1,520 million in 2025 and projected to reach about USD 12,700 million by 2032, growing at a CAGR of 35.0%. The United States dominates, hosting NVIDIA (InfiniBand, Spectrum-X, NVLink, ConnectX, BlueField), Broadcom (Tomahawk, Jericho, CPO), Arista (18.9% DC Ethernet share, USD 2.2B Q2 2025 revenue), Cisco (USD 2B+ AI orders FY2025), Marvell (800G DSPs), and Coherent (optical transceivers). Northern Virginia, Dallas, and Silicon Valley concentrate the largest AI training clusters and the highest density of 800G deployments. Canada contributes through growing AI cluster construction.

Europe

Europe grows strongly, valued at approximately USD 579 million in 2025 and forecast to reach around USD 5,150 million by 2032, expanding at a CAGR of 37.0%. The Netherlands (Amsterdam) is Europe's largest data center networking hub. The United Kingdom brings AI cluster networking demand from its DeepMind and startup ecosystem. Germany contributes through enterprise AI infrastructure and automotive AI networking. The Nordics bring hyperscaler campus networking in renewable-energy-powered facilities.

Asia Pacific

Asia Pacific grows at the same rate as Europe, valued at roughly USD 1,267 million in 2025 and projected to reach about USD 11,500 million by 2032, growing at a CAGR of 37.0%. China is the largest APAC market, with Innolight and Eoptolink as major 800G optical module manufacturers and a domestic AI networking ecosystem operating under export-control constraints. Taiwan contributes through Celestica and Edgecore networking hardware manufacturing. Japan brings enterprise AI cluster networking. India and Singapore add AI data center networking demand.

Rest of World

The Rest of World market reached an estimated USD 254 million in 2025 and is projected to hit about USD 2,500 million by 2032, growing at a CAGR of 39.0%. The Middle East leads as UAE and Saudi Arabia build sovereign AI training clusters requiring full 800G/1.6T networking infrastructure, representing the fastest-growing deployment region for AI interconnect.

Regional outlook summary:

• North America holds the largest base on NVIDIA/Broadcom/Arista/Cisco headquarters and hyperscaler cluster density.
• Rest of World grows fastest on Gulf sovereign AI infrastructure buildout.
• Asia Pacific grows on Chinese optical module manufacturing and Taiwan networking hardware.
• Europe grows on Amsterdam data center hub and EU AI infrastructure investment.
• 800G/1.6T deployment cadence, InfiniBand vs. Ethernet mix, and optical module supply are the universal variables.

Key Company Insights

The competitive landscape spans four tiers: full-stack networking vendors (switch + adapter + DPU), switch silicon providers, optical module/transceiver manufacturers, and laser/component suppliers. The leading players include NVIDIA, Broadcom, Arista, Cisco, Marvell, Coherent, Lumentum, Innolight, Intel, AMD, Juniper, Applied Optoelectronics, Eoptolink, Celestica, and Edgecore.

• NVIDIA (InfiniBand Quantum-X800 / Spectrum-X / NVLink / ConnectX-8 / BlueField-3)
• Broadcom (Tomahawk 5 / Jericho3-AI / CPO)
• Arista Networks (AI Spine / 7800R4)
• Cisco (Silicon One G200 / Nexus AI)
• Marvell Technology (800G DSPs / Alaska PHYs)
• Coherent Corp (800G/1.6T Optical Transceivers)
• Lumentum Holdings (Lasers / Optical Components)
• Innolight Technology (800G Optical Modules)
• Intel (Gaudi Interconnect / IPU / Silicon Photonics)
• AMD (Infinity Fabric / Pensando DPU)
• Juniper Networks (HPE Acquisition / AI Fabric)
• Applied Optoelectronics (AOI / Laser Components)
• Eoptolink Technology (800G Chinese Optical Modules)
• Celestica (Networking Hardware / Switch Assembly)
• Edgecore Networks (Open Networking / AI Switches)

NVIDIA holds the broadest AI interconnect portfolio: Quantum-X800 InfiniBand (144 × 800 Gb/s with SHARP v4), Spectrum-X AI Ethernet, ConnectX-8 SuperNIC (800 Gb/s, PCIe Gen 6), BlueField-3 DPU (400 Gb/s, 16 Arm cores), and NVLink 5 / NVLink Switch (1.8 TB/s per GPU, 576-GPU domains). NVIDIA's data center Ethernet switch share surged from under 4% to 21.5% in one year, and its annualized networking business reaches approximately USD 60 billion. The ability to sell GPU + network + DPU as an integrated stack is the bundling advantage that defines the competitive landscape.

Broadcom provides the switch silicon (Tomahawk 5, Jericho3-AI) that powers Arista, Cisco, and most non-NVIDIA AI Ethernet switches, plus CPO technology that promises to reduce interconnect power by 50%+. Arista holds 18.9% of data center Ethernet share (Q2 2025) and is the leading independent AI spine switch vendor. Cisco received over USD 2 billion in AI infrastructure orders in FY2025. Marvell leads in 800G DSPs for optical transceivers, enjoying a strong market position in 800-gigabit interconnects.

Among optical companies, Coherent, Lumentum, Innolight, and Eoptolink supply the 800G and 1.6T transceivers that every AI cluster requires. 1.6T demand in 2026 is projected to reach 11 million units, driven primarily by NVIDIA and Google procurement.

Key company strategy conclusions:

• NVIDIA holds the broadest AI interconnect portfolio (InfiniBand + Ethernet + NVLink + DPU) and surged to 21.5% DC Ethernet share.
• Broadcom provides the switch silicon for most non-NVIDIA AI Ethernet switches plus CPO leadership.
• Arista leads independent AI Ethernet spine switches at 18.9% DC share.
• Cisco doubled its AI networking target to USD 2B+ in FY2025.
• Optical module supply (Coherent, Innolight, Eoptolink) is the capacity bottleneck that determines deployment speed.

Recent Developments

• In June 2025, the Ultra Ethernet Consortium (UEC) finalized the UEC 1.0 specification, reconstructing the Ethernet stack to close the latency gap with InfiniBand for AI workloads—backed by AMD, Arista, Broadcom, Cisco, Intel, Meta, and Microsoft.¹
• At Computex 2025, NVIDIA announced NVLink 5 delivering 1.8 TB/s per GPU (18 connections at 100 GB/s each) with NVLink Switch enabling 130 TB/s aggregate bandwidth across up to 576 GPUs.²
• In FY2025, Cisco received over USD 2 billion in AI infrastructure orders from webscale customers, more than doubling its original USD 1 billion target.4
• In August 2025, NVIDIA and Broadcom simultaneously introduced the Scale-Across concept, aiming to extend GPU interconnect across multiple data centers for larger-scale distributed training.5

Sources

• ¹ TrendForce, "InfiniBand vs Ethernet: Broadcom and NVIDIA Scale-Out Tech War," October 2025; ValueAddVC, "AI Networking Vendors Ranked 2026," July 2026
• ² Network World, "Buyer's Guide to AI Networking Technology," November 2025 — NVLink 5 Computex 2025
• ³ ValueAddVC, "AI Networking Vendors Ranked 2026," July 2026 — Q1 2026 Ethernet switch market
• 4 Network World, November 2025 — Cisco FY2025 AI orders
• 5 TrendForce, October 2025 — NVIDIA/Broadcom Scale-Across August 2025

Real-World Use Cases

NVIDIA's Quantum-X800 InfiniBand deployment across hyperscaler AI training clusters demonstrated why InfiniBand commands a revenue-per-port premium in the AI interconnect market. The Quantum-X800 delivers 144 × 800 Gb/s ports with SHARP v4 in-network computing that offloads collective operations (all-reduce, all-gather) from GPUs to the switch ASIC itself, reducing network traffic by 50% and delivering 10–20% training performance improvement. GPUDirect RDMA enables direct GPU-to-GPU data transfer without CPU involvement, achieving 10x better performance than CPU-mediated transfers. For training clusters running 50,000+ GPUs on Blackwell Ultra, the combination of InfiniBand's 1–2 microsecond latency, SHARP offload, and GPUDirect RDMA translates directly into faster training time—and at USD 2.49–3.37 per GPU-hour, even a 10% training speedup across a months-long run saves millions of dollars.6

The Ultra Ethernet Consortium's UEC 1.0 deployment in production AI clusters confirmed that Ethernet can compete with InfiniBand for AI networking when configured with AI-specific enhancements. UEC 1.0 introduces packet spraying for multipath load balancing, enhanced congestion notification, and support for collective communication offloads—features that standard Ethernet does not provide but that AI training workloads require. Early production deployments at UEC member organizations (Meta, Microsoft) demonstrated that UEC 1.0 Ethernet achieved latency and throughput within measurable range of InfiniBand for most distributed training configurations, while preserving the multi-vendor interoperability and operational familiarity that Ethernet teams already possess. The deployment confirmed that the InfiniBand vs. Ethernet contest is converging toward performance parity—and that the winner for any given cluster will depend on whether the buyer values NVIDIA's integrated stack (InfiniBand) or the open, multi-vendor ecosystem (UEC Ethernet).7

Sources

• 6 Network World, "Buyer's Guide to AI Networking Technology," November 2025; Introl, "800G Networking for AI," March 2026
• 7 TrendForce, "InfiniBand vs Ethernet," October 2025; ValueAddVC, "AI Networking Vendors Ranked 2026," July 2026

Market Segmentation

The AI interconnect market segments across four interlocking axes. By interconnect technology, it spans InfiniBand, AI Ethernet, NVLink/NVSwitch, optical transceivers, DPUs/SmartNICs, and switch silicon—six technology categories that compose the full networking stack. By interconnect scope, it covers intra-node (NVLink, PCIe), inter-node (InfiniBand, Ethernet), inter-rack (800G/1.6T fabric), and inter-data-center (Scale-Across, DCI). By deployment context, it serves hyperscale training clusters, enterprise AI, specialized GPU clouds, and colocation AI tenants. By region, investment follows where GPU clusters are concentrated and where optical module manufacturing resides.

These axes interlock: a hyperscaler building a 50,000-GPU Blackwell Ultra training cluster uses NVLink 5 (intra-node), Quantum-X800 InfiniBand (inter-node), 800G Coherent optical transceivers (inter-rack), and BlueField-3 DPUs (network offload)—four technology categories across three interconnect scopes in a single deployment.

Segmentation summary:

• InfiniBand leads training revenue; AI Ethernet grows fastest on UEC 1.0 and multi-vendor demand.
• Hyperscale clusters lead expenditure; enterprise AI grows fastest by customer count.
• 800G is the 2025 standard; each bandwidth generation requires full fabric replacement.
• NVLink 5 dominates intra-node; InfiniBand and Ethernet contest inter-node.
• Optical module supply and laser capacity define the production bottleneck.

Conclusion and Future Outlook

Through 2032, AI interconnect will be recognized as the binding constraint that determines how large AI can scale—more decisive for cluster performance than any individual GPU, because every GPU in the cluster depends on every other GPU through the network that connects them. The forces driving the market—GPU clusters scaling to 100,000+ nodes, 800G → 1.6T → 3.2T bandwidth generations requiring full network refresh, the InfiniBand vs. Ethernet contest creating investment from both sides, and optical transceiver demand surging to tens of millions of 800G+ units—are structural and self-reinforcing. The technology will advance: CPO will integrate optics onto the switch ASIC, reducing power and increasing density; OCS (optical circuit switching) will provide stable, circuit-like data flows for AI collectives; and CXL will pool memory across nodes, blurring the boundary between intra-node and inter-node interconnect.

The competitive landscape will be defined by whether NVIDIA's integrated GPU+network stack captures the market, or whether the UEC Ethernet coalition preserves a multi-vendor alternative. For AI infrastructure architects, network engineers, hyperscalers, and investors, the AI interconnect market is the bandwidth layer that scales or constrains every AI workload—and the organizations that invest in the right interconnect architecture today will train faster, serve cheaper, and scale further than those that treat networking as an afterthought.

Frequently Asked Questions (FAQ)

1. How big is the AI interconnect market?

The AI interconnect market was estimated at roughly USD 3,620 million in 2025 and is projected to reach about USD 31,850 million by 2032. North America accounts for the largest share, concentrating hyperscaler training clusters and the headquarters of NVIDIA, Broadcom, Arista, and Cisco.

2. What is the AI interconnect market growth rate?

The market is forecast to grow at a CAGR of approximately 37% from 2026 to 2032. Rest of World is the fastest-growing region at around 39%, driven by Gulf sovereign AI infrastructure; Asia Pacific and Europe grow strongly at 37%.

3. Which segment leads the AI interconnect market?

By technology, InfiniBand leads AI training network revenue. AI-optimized Ethernet is the fastest-growing on UEC 1.0 and multi-vendor openness. By scope, inter-node (scale-out) leads; inter-data-center (Scale-Across) grows fastest.

4. Who are the key players in the AI interconnect market?

Leading companies include NVIDIA (InfiniBand, Spectrum-X, NVLink), Broadcom (Tomahawk, Jericho, CPO), Arista Networks, Cisco, Marvell, Coherent, Lumentum, Innolight, Intel, AMD, Juniper, Applied Optoelectronics, Eoptolink, Celestica, and Edgecore.

5. What are the factors driving the AI interconnect market?

The primary drivers are GPU clusters scaling to 100,000+ nodes demanding proportionally more network bandwidth, data center Ethernet switching growing 39.8% YoY to USD 15.4B, 800G as the 2025 standard with 1.6T arriving in 2027, and NVIDIA's singular GPU+network bundling driving share gains.

Speak With Our Analyst

The AI interconnect market is the bandwidth layer that scales or constrains every AI workload, and the segment-level detail on InfiniBand vs. Ethernet economics, optical transceiver supply dynamics, switch silicon competition, and vendor lock-in considerations is where strategic decisions are won or lost. MarketsandMarkets can help you go deeper: request a sample of the full study, speak with our analyst about your specific questions, or customize the scope to your target interconnect technologies, deployment contexts, and geographies. Reach out to explore how this intelligence can inform your networking architecture, vendor selection, or investment decisions.