스케일업 AI 네트워킹 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the scale-up AI networking market size was valued at USD 18.76 billion in 2025 and estimated to grow from USD 28.64 billion in 2026 to reach USD 82.43 billion by 2031, at a CAGR of 23.54% during the forecast period (2026-2031).

This report is Segmented by Offering (Hardware, Software, and Services), Fabric Technology (Proprietary Accelerator Scale-Up Fabrics, Open Scale-Up Fabrics, and More), Domain Size (Up To 8 Accelerators, 9 To 72 Accelerators, and More), Workload (AI Training, AI Inference, and More), End User (Hyperscale Cloud Providers, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Scale-Up AI Networking Market Trends and Insights

Rapid Expansion of AI Training Clusters

The scale-up AI networking market is being pushed forward by the rapid increase in AI training cluster size, because larger synchronous workloads make network reliability a central design requirement rather than a secondary one. OpenAI said its Multipath Reliable Connection protocol was deployed across its largest NVIDIA GB200 supercomputers to address the network interruptions that had previously forced cluster-wide restarts during training. The decision to contribute that protocol to the Open Compute Project shows that the scale-up AI networking market is moving toward shared methods for reliability as frontier training systems become more complex. This also changes vendor positioning, because suppliers that can support scale-up, scale-out, and operational software together are better placed when cluster architectures expand across racks, rows, and sites. As a result, the scale-up AI networking market is widening beyond a component sale and moving closer to a full-stack infrastructure decision.

Migration From Proprietary Links to Open Ethernet Fabrics

The scale-up AI networking market is also gaining support from the move toward open Ethernet fabrics, because buyers want broader interoperability across accelerators, switches, and control software. The Open Compute Project launched the ESUN initiative to define Ethernet for scale-up AI infrastructure as a standards-based community effort with participation from major silicon, cloud, and systems vendors. The Ultra Ethernet Consortium formalized an Ethernet-based communication stack for AI and HPC workloads, then updated it in 2026, which helped turn open Ethernet from a concept into a compliance-driven path. In the scale-up AI networking market, that matters because customers adopting AMD, custom silicon, or mixed environments need a fabric that is not tied to a single accelerator roadmap. It also means differentiation is shifting away from closed connectivity alone and toward integration quality, automation, and the ability to prove interoperability at scale.

High Power and Cooling Load at Scale-Up Layer

Power and cooling pressure is one of the clearest restraints on the scale-up AI networking market because network equipment now sits inside much denser AI infrastructure environments than conventional data halls were built for. Applied Thermal Engineering and IEEE ITherm both point to liquid cooling as a necessary direction, but they also note added complexity in plumbing, coolant compatibility, and long-term serviceability. Ciena said its pluggable CPO approach can reduce power consumption by up to 70%, which shows how strongly the scale-up AI networking market is now being shaped by power-performance tradeoffs rather than bandwidth alone. This favors greenfield campuses that can be designed around new thermal requirements, while retrofits in older facilities face longer readiness cycles and higher integration risk. It also means fabric decisions are becoming tied more closely to site engineering and deployment sequencing.

Other drivers and restraints analyzed in the detailed report include:

1. Higher Rack-Level Bandwidth Density Requirements
2. Co-Packaged Optics and Silicon Photonics Adoption
3. Supply Chain Dependence for High-Speed Switch Silicon and Optics

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Hardware held 90.11% of revenue in 2025, which kept the physical layer as the main spending center of the scale-up AI networking market. That mix reflects the capital intensity of switches, ASICs, network interface cards, cables, and optical components required to stand up new AI clusters. It also shows that much of the scale-up AI networking market is still in a build phase where buyers first secure bandwidth, topology, and reliability at the physical layer before widening spend into orchestration. Software is the fastest-growing offering at a 24.21% CAGR through 2031, as operators add telemetry, congestion control, and automation tools around existing fabric deployments.

The software case is becoming stronger because the scale-up AI networking industry is moving toward more complex and distributed cluster operations. The Ultra Ethernet Consortium's specification provides a standards-driven software and transport framework for AI and HPC environments, which supports wider use of common operational methods across vendors. Google also showed through its Matryoshka network design system that model-driven management can support large data center estates over multiple years, which reinforces the long-run value of software layers in the scale-up AI networking market. Services remain smaller in revenue, but they rise with integration complexity because multi-site AI factory architectures need specialized design, commissioning, and ongoing support.

Proprietary accelerator scale-up fabrics held 85.33% of revenue in 2025, which reflected the installed base advantage of tightly integrated accelerator ecosystems in the scale-up AI networking market. That lead came from the technical and commercial strength of hardware-software co-design, especially where buyers wanted the shortest path to large training system deployment. Open scale-up fabrics are the fastest-growing segment at a 24.62% CAGR through 2031, which shows that customers are looking for alternatives as non-proprietary accelerator options gain traction. The scale-up AI networking market size for open scale-up fabrics is rising with demand for architectures that can work across broader silicon choices and longer procurement cycles.

AMD and Celestica said the Helios rack-scale AI platform will use Ultra Accelerator Link over Ethernet for scale-up connectivity, which gives the open fabric segment a clearer product path rather than just a standards narrative. NVIDIA responded with NVLink Fusion, which extends its ecosystem by allowing third-party custom XPUs to integrate through NVLink chiplets instead of leaving that adjacent space uncontested. Ethernet-based scale-up fabrics are also becoming more visible through ESUN and Arista's 7060XE7 platforms, while early optical I/O approaches such as Ayar Labs' optical chiplet work remain earlier in the development cycle. In the scale-up AI networking market, fabric competition is now defined less by raw connectivity alone and more by roadmap control, interoperability, and ecosystem depth.

Complete Report Scope:

• By Offering
  • Hardware
  • Software
  • Services
• By Fabric Technology
  • Proprietary Accelerator Scale-Up Fabrics
  • Open Scale-Up Fabrics
  • Ethernet-Based Scale-Up Fabrics
  • Other Emerging Scale-Up Fabrics
• By Scale-Up Domain Size
  • Up to 8 Accelerators
  • 9 to 72 Accelerators
  • 73 to 256 Accelerators
  • Above 256 Accelerators
• By Workload
  • AI Training
  • AI Inference
  • Fine-Tuning and Model Adaptation
  • HPC and Scientific Computing
  • Other Workloads
• By End User
  • Hyperscale Cloud Providers
  • AI Cloud and GPU-as-a-Service Providers
  • Enterprise Data Centers
  • Government, Research, and HPC Centers
  • Colocation Data Centers
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 58.44% of the scale-up AI networking market share in 2025, which kept it as the largest regional contributor. The region remains the center of the scale-up AI networking market because it combines hyperscaler capital, accelerator ecosystem leadership, and strong participation in open standards efforts. The United States accounts for most of that demand, while Canada adds research-led activity and Mexico supports emerging colocation and nearshore infrastructure interest. North America also has an advantage in vendor proximity, because several of the companies and industry groups shaping Ethernet, UALink, and rack-scale AI systems are closely tied to the regional ecosystem.

Asia-Pacific is the second-largest geography in the scale-up AI networking market, with demand spread across China, Japan, South Korea, India, and Southeast Asia. Huawei's June 2026 launch of 10 AI-Optical Network products at MWC Shanghai showed that China is pushing actively into AI-centric optical and network infrastructure development. Japan remains technically important through sovereign computing priorities and optical interconnect work, while South Korea supports the broader ecosystem through its memory and semiconductor base. India and Southeast Asia are growing parts of the scale-up AI networking market because regional digital infrastructure buildouts are expanding alongside AI deployment ambitions.

Europe and the Middle East and Africa show different demand patterns within the scale-up AI networking market. Europe recorded solid momentum in 2025, with Germany standing out as a major data center location and as a recipient of federal support for at least one AI Gigafactory. The region's growth path is tied to sovereign compute priorities, compliance requirements, and longer public-private procurement cycles. The Middle East and Africa is the fastest-growing geography at a 24.42% CAGR through 2031, supported by sovereign AI investment and large campus-scale development plans. The Stargate UAE project, with total cost exceeding USD 30 billion and Phase 1 expected in Q3 2026, shows how quickly the region is moving from policy ambition to physical infrastructure commitment. South America remains earlier in development, with demand centered on colocation growth and domestic infrastructure programs that are still building scale.

1. NVIDIA Corporation
2. Broadcom Inc.
3. Cisco Systems, Inc.
4. Arista Networks, Inc.
5. Marvell Technology, Inc.
6. Hewlett Packard Enterprise Company
7. Juniper Networks, Inc.
8. Huawei Technologies Co., Ltd.
9. Dell Technologies Inc.
10. Intel Corporation
11. Celestica Inc.
12. Accton Technology Corporation
13. Foxconn Interconnect Technology Limited
14. Quanta Computer Inc.
15. Nokia Corporation
16. Extreme Networks, Inc.
17. NEC Corporation
18. ZTE Corporation
19. Ciena Corporation
20. Coherent Corp.
21. Lumentum Holdings Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rapid Expansion of AI Training Clusters
  • 4.2.2 Migration From Proprietary Links To Open Ethernet Fabrics
  • 4.2.3 Higher Rack-Level Bandwidth Density Requirements
  • 4.2.4 Co-Packaged Optics And Silicon Photonics Adoption
  • 4.2.5 AI-Ready Network Automation And Telemetry Adoption
  • 4.2.6 Sovereign AI Infrastructure Buildouts In Key Markets
• 4.3 Market Restraints
  • 4.3.1 High Power And Cooling Load At Scale-Up Layer
  • 4.3.2 Supply Chain Dependence For High-Speed Switch Silicon And Optics
  • 4.3.3 Interoperability Gaps Between Vendor Ecosystems
  • 4.3.4 Immature Standards Across Emerging Scale-Up Architectures
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Bargaining Power of Buyers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Offering
  • 5.1.1 Hardware
  • 5.1.2 Software
  • 5.1.3 Services
• 5.2 By Fabric Technology
  • 5.2.1 Proprietary Accelerator Scale-Up Fabrics
  • 5.2.2 Open Scale-Up Fabrics
  • 5.2.3 Ethernet-Based Scale-Up Fabrics
  • 5.2.4 Other Emerging Scale-Up Fabrics
• 5.3 By Scale-Up Domain Size
  • 5.3.1 Up to 8 Accelerators
  • 5.3.2 9 to 72 Accelerators
  • 5.3.3 73 to 256 Accelerators
  • 5.3.4 Above 256 Accelerators
• 5.4 By Workload
  • 5.4.1 AI Training
  • 5.4.2 AI Inference
  • 5.4.3 Fine-Tuning and Model Adaptation
  • 5.4.4 HPC and Scientific Computing
  • 5.4.5 Other Workloads
• 5.5 By End User
  • 5.5.1 Hyperscale Cloud Providers
  • 5.5.2 AI Cloud and GPU-as-a-Service Providers
  • 5.5.3 Enterprise Data Centers
  • 5.5.4 Government, Research, and HPC Centers
  • 5.5.5 Colocation Data Centers
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 India
    • 5.6.3.5 Southeast Asia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Broadcom Inc.
  • 6.4.3 Cisco Systems, Inc.
  • 6.4.4 Arista Networks, Inc.
  • 6.4.5 Marvell Technology, Inc.
  • 6.4.6 Hewlett Packard Enterprise Company
  • 6.4.7 Juniper Networks, Inc.
  • 6.4.8 Huawei Technologies Co., Ltd.
  • 6.4.9 Dell Technologies Inc.
  • 6.4.10 Intel Corporation
  • 6.4.11 Celestica Inc.
  • 6.4.12 Accton Technology Corporation
  • 6.4.13 Foxconn Interconnect Technology Limited
  • 6.4.14 Quanta Computer Inc.
  • 6.4.15 Nokia Corporation
  • 6.4.16 Extreme Networks, Inc.
  • 6.4.17 NEC Corporation
  • 6.4.18 ZTE Corporation
  • 6.4.19 Ciena Corporation
  • 6.4.20 Coherent Corp.
  • 6.4.21 Lumentum Holdings Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment