GPU 액랭 시장 규모, 점유율, 산업 분석 : 냉각 방식별, 냉각 레벨별, 전력 밀도별, 전개 방식별, 지역별 전망 및 예측(2026-2033년)

The Global GPU Liquid Cooling Market is expected to reach USD 37.2 billion by 2033, growing at a CAGR of 24.5% during (2026 - 2033).

Rising demand for efficient thermal management, higher computing density, reduced operating temperatures, reliable GPU performance, and lower energy consumption is driving the GPU Liquid Cooling Market across AI, HPC, cloud data centers, gaming, and enterprise infrastructure. Growing generative AI workloads, rack-scale GPU deployments, hyperscale data center expansion, and pressure to improve power usage effectiveness are strengthening market adoption. Demand is increasing for direct-to-chip cooling, immersion cooling, coolant distribution units, leak detection, modular designs, and energy-efficient thermal architectures. The market originated as increasingly powerful GPUs created heat loads beyond the limits of conventional air-cooling systems. Initially, GPU infrastructure relied mainly on air cooling for graphics processing, AI workloads, scientific simulations, and high-performance computing applications.

Key Market Trends & Insights

• By cooling type, Single-Phase dominated the market in 2025 with USD 4.9 billion and is projected to reach USD 27.2 billion by 2033, growing at a CAGR of 24.1% during (2026 - 2033).
• Two-Phase is expected to grow faster by cooling type, registering a CAGR of 25.7% during (2026 - 2033), supported by rising GPU thermal design power, higher heat-transfer needs, and advanced high-density cooling requirements.
• By cooling level, Component-Level Cooling dominated the market in 2025 with USD 4.1 billion and is expected to reach USD 22.9 billion by 2033, growing at a CAGR of 24.2% during (2026 - 2033).
• Server Rack-Level Cooling is expected to grow faster by cooling level, registering a CAGR of 25.1% during (2026 - 2033), driven by rising rack power densities, AI clusters, CDUs, and rack-scale thermal management systems.
• By power density, 300 W-700 W dominated the market in 2025 with USD 3.4 billion and is projected to reach USD 18.9 billion by 2033, growing at a CAGR of 23.9% during (2026 - 2033).
• Above 700 W is projected to reach USD 12.1 billion by 2033, growing at a CAGR of 25.2% during (2026 - 2033), supported by next-generation accelerators, high-density AI servers, and direct-to-chip or immersion cooling demand.
• By deployment, Hyperscale Cloud dominated the market in 2025 with USD 3.4 billion and is projected to reach USD 18.8 billion by 2033, growing at a CAGR of 23.9% during (2026 - 2033).
• Regionally, Asia Pacific dominated the market in 2025 with USD 2,463.96 million and is projected to reach USD 14.4 billion by 2033, while LAMEA is expected to grow fastest with a CAGR of 26.4% during (2026 - 2033).

The GPU Liquid Cooling Market is witnessing strong expansion as AI, HPC, cloud, and enterprise workloads increasingly depend on dense GPU clusters that generate high thermal loads. Liquid cooling helps operators reduce thermal throttling, improve hardware reliability, support higher rack densities, and lower cooling-related power consumption. Growth is further supported by the shift from retrofit cooling toward integrated server and rack-scale designs, where cold plates, CDUs, manifolds, facility-side heat rejection, and monitoring systems are planned together. The market is also benefiting from modular and scalable solutions that help existing and new data centers transition toward liquid-cooled GPU infrastructure.

The GPU Liquid Cooling Market is moderately fragmented and innovation-driven, with competition led by thermal management providers, liquid-cooling specialists, data center infrastructure companies, integrated server vendors, and rack-scale AI infrastructure suppliers. Vendors compete through cold plate efficiency, coolant distribution performance, leak prevention, modularity, AI-driven monitoring, direct-to-chip integration, immersion capabilities, deployment support, and compatibility with next-generation GPU platforms. Future competition is expected to depend on complete cooling ecosystems, advanced materials, facility-level integration, energy optimization, scalable CDUs, and the ability to support increasingly powerful GPU architectures.

Drivers

• Increasing Computational Demands from AI and GPU-Accelerated Workloads
• Shift Towards Energy Efficiency and Sustainable Data Center Operations
• Technological Advancements and Customization in Liquid Cooling Solutions
• Growing Industry Recognition of Liquid Cooling's Strategic Value

Restraints

• High Initial Capital Investment and Integration Complexity
• Regulatory and Safety Constraints on Cooling Fluids
• Limited Standardization and Interoperability Challenges

Opportunities

• Direct-to-Chip Liquid Cooling Driving Next-Generation GPU Performance
• Expansion into Immersion Cooling for Enhanced Thermal Efficiency and System Reliability
• Integration of AI-Driven Thermal Management Systems to Optimize Liquid Cooling Performance

Challenges

• High Initial Capital Expenditure and Integration Costs
• Thermal Management and Reliability Challenges Under High Workloads
• Regulatory and Environmental Compliance Constraints

Market Share Analysis

The global GPU Liquid Cooling Market is moderately fragmented, with competition led by Vertiv Holdings, CoolIT Systems, Schneider Electric, Super Micro Computer, Boyd Corporation, and nVent Electric. Vertiv leads through end-to-end thermal infrastructure, while CoolIT holds a strong specialist position in direct-to-chip cooling. Schneider Electric strengthens the market through liquid cooling, power, controls, and AI factory infrastructure. Supermicro, Boyd, Delta Electronics, Modine, LiquidStack, and Rittal further support competition through GPU server integration, cold plates, CDUs, immersion cooling, rack infrastructure, and high-density data center cooling solutions.

Cooling Type Outlook

Based on Cooling Type, the market is segmented into Single-Phase and Two-Phase. The Single-Phase market dominated the Global GPU Liquid Cooling Market by Cooling Type in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 27.2 billion by 2033, growing at a CAGR of 24.1 % during the forecast period. The Two-Phase market is expected to witness a CAGR of 25.7% during (2026 - 2033).

Single-Phase leads the market due to its mature cold-plate systems, relatively simple coolant circulation, easier serviceability, and compatibility with existing data center designs. It is widely used across AI, HPC, cloud, and enterprise GPU deployments where stable heat removal and operational reliability are essential. Two-Phase cooling is gaining attention as GPU power density rises and thermal loads become harder to manage through conventional approaches. Its stronger heat-transfer capability, phase-change mechanism, dielectric fluids, and evaporative cooling potential make it attractive for future high-density accelerator environments.

Cooling Level Outlook

Based on Cooling Level, the market is segmented into Component-Level Cooling and Server Rack-Level Cooling. The Component-Level Cooling market dominated the Global GPU Liquid Cooling Market by Cooling Level in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 22.9 billion by 2033, growing at a CAGR of 24.2 % during the forecast period. The Server Rack-Level Cooling market is expected to witness a CAGR of 25.1% during (2026 - 2033).

Component-Level Cooling dominates the market as data center operators require direct heat extraction from GPUs, processors, memory, voltage regulators, and other high-heat components. Cold plates and targeted cooling technologies improve thermal efficiency and allow GPUs to operate more reliably under intense workloads. Server Rack-Level Cooling is expanding as AI and HPC deployments increase rack power density. Integrated coolant distribution units, liquid loops, manifolds, and rack-scale cooling systems are becoming important as operators reduce dependence on conventional air cooling and prepare infrastructure for higher-density GPU clusters.

Power Density Outlook

Based on Power Density, the market is segmented into 300 W-700 W, Above 700 W, and Below 300 W. The 300 W-700 W market dominated the Global GPU Liquid Cooling Market by Power Density in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 18.9 billion by 2033, growing at a CAGR of 23.9 % during the forecast period. The Above 700 W market is expected to witness a CAGR of 25.2% during (2026 - 2033). Additionally, The Below 300 W market is expected to witness highest CAGR of 25% during (2026 - 2033).

The 300 W-700 W segment leads the market due to the widespread use of high-performance GPUs across AI training, inference, visualization, rendering, and HPC workloads. These deployments require better thermal control than air cooling can provide while still allowing practical liquid-cooling integration. Above 700 W is gaining strong demand as next-generation accelerators, high-density AI servers, and large GPU clusters generate substantially higher heat loads. Below 300 W remains smaller because air cooling continues to be economically viable for many lower-density GPU installations, though liquid cooling can still support stability and noise reduction in specialized use cases.

Deployment Outlook

Based on Deployment, the market is segmented into Hyperscale Cloud, Enterprise, Government and Research HPC, and Edge AI. The Hyperscale Cloud market dominated the Global GPU Liquid Cooling Market by Deployment in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 18.8 billion by 2033, growing at a CAGR of 23.9 % during the forecast period. The Enterprise market is expected to witness a CAGR of 24.8% during (2026 - 2033). Additionally, The Government and Research HPC market is expected to witness highest CAGR of 25.3% during (2026 - 2033).

Hyperscale Cloud dominates the market due to rapid AI infrastructure expansion, large GPU clusters, and pressure to improve power usage effectiveness while managing rising thermal loads. Enterprise demand is increasing as organizations deploy private AI infrastructure, accelerated computing, analytics, and GPU-based workloads. Government and Research HPC remains important due to supercomputing, simulations, weather modeling, scientific research, and computational workloads requiring high-density GPU environments. Edge AI is emerging as localized inference, robotics, smart factories, telecom edge nodes, and real-time analytics create demand for compact GPU thermal management.

Regional Outlook

Region-wise, the GPU Liquid Cooling Market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America market dominated the Global GPU Liquid Cooling Market by Region in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 11.3 billion by 2033, growing at a CAGR of 23.8 % during the forecast period. The Europe market is expected to witness a CAGR of 24.3% during (2026 - 2033). Additionally, The Asia Pacific market is expected to witness a CAGR of 25% during (2026 - 2033).

Asia Pacific dominates the market due to expanding data center capacity, AI computing infrastructure, semiconductor ecosystems, and investments in high-density cloud and HPC facilities. North America is supported by hyperscale cloud providers, accelerated computing deployments, AI infrastructure expansion, and strong demand for rack-scale GPU cooling. Europe benefits from data center efficiency requirements, sustainability initiatives, HPC investments, and adoption of advanced thermal management technologies. LAMEA is gradually expanding through cloud infrastructure development, emerging data center projects, and growing AI and high-performance computing adoption.

Recent Strategies Deployed in the Market

• 2026-June: Vertiv completed the acquisition of ThermoKey S.p.A. to expand heat-rejection and heat-exchange capabilities for end-to-end thermal architectures in high-density AI data centers.
• 2023-May: KKR entered into a definitive agreement to acquire CoolIT Systems, supporting the company's ability to scale liquid-cooling technologies for HPC, cloud, enterprise, and data center environments.
• 2025-May: Supermicro launched DLC-2 direct liquid cooling for NVIDIA Blackwell GPU infrastructure, extending cold-plate coverage across GPUs, CPUs, memory, PCIe switches, and voltage regulators.
• 2024-November: CoolIT Systems introduced a high-density liquid-to-liquid coolant distribution unit to support rising thermal loads from AI accelerators and high-performance computing systems.
• 2025-September: Schneider Electric launched NVIDIA GB300 NVL72 liquid-cooling reference designs, supporting AI clusters with integrated power, liquid cooling, controls, and data hall planning.
• 2025-November: nVent expanded its modular liquid-cooling portfolio with enhanced CDUs, rack- and row-based configurations, advanced manifolds, and updated rack infrastructure for next-generation AI chips.

List of Key Companies Profiled

• Vertiv Holdings Co.
• CoolIT Systems Inc.
• Schneider Electric SE
• Super Micro Computer, Inc.
• Boyd Corporation
• nVent Electric plc
• Delta Electronics, Inc.
• Modine Manufacturing Company
• LiquidStack
• Rittal GmbH & Co. KG

Global GPU Liquid Cooling Market Report Segmentation

By Cooling Type

• Single-Phase
• Two-Phase

By Cooling Level

• Component-Level Cooling
• Server Rack-Level Cooling

By Power Density

• 300 W-700 W
• Above 700 W
• Below 300 W

By Deployment

• Hyperscale Cloud
• Enterprise
• Government and Research HPC
• Edge AI

By Geography

• North America
  • US
  • Canada
  • Mexico
  • Rest of North America
• Europe
  • Germany
  • UK
  • France
  • Russia
  • Spain
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Singapore
  • Taiwan
  • Rest of Asia Pacific
• LAMEA
  • Brazil
  • Argentina
  • UAE
  • Saudi Arabia
  • South Africa
  • Nigeria
  • Rest of LAMEA

Table of Contents

Chapter 1. Research Scope & Methodology

• 1.1 Market Definition
• 1.2 Analysis Period & Currencys
• 1.3 Segmentation
• 1.4 GPU Liquid Cooling Market, by Geography
• 1.5 Research Methodology

Chapter 2. Market Overview

• 2.1 COVID-19 Impact
• 2.2 Market Composition and Scenario

Chapter 3. Key Factors Impacting Market

• 3.1 Market Drivers
• 3.2 Market Restraints
• 3.3 Market Opportunities
• 3.4 Market Challenges
• 3.5 Market Trends
• 3.6 State of Competition
• 3.7 Market Consolidation
• 3.8 Key Customer Criteria

Chapter 4. Product Life Cycle

Chapter 5. Value Chain Analysis of GPU Liquid Cooling Market

Chapter 6. Competition Analysis - Global

• 6.1 Market Share Analysis
• 6.2 Recent Developments
  • 6.2.1 Mergers & Acquisitions
  • 6.2.2 Product Launch & Product Expansion
  • 6.2.3 Partnership, Collaboration & Agreements
  • 6.2.4 Geographical Expansion

Chapter 7. Segmentation By Cooling Type

• 7.1 Single-Phase
• 7.2 Two-Phase

Chapter 8. Segmentation By Cooling Level

• 8.1 Component-Level Cooling
• 8.2 Server Rack-Level Cooling

Chapter 9. Segmentation By Power Density

• 9.1 Below 300 W
• 9.2 W-700 W
• 9.3 Above 700 W

Chapter 10. Segmentation By Deployment

• 10.1 Hyperscale Cloud
• 10.2 Enterprise
• 10.3 Government and Research HPC
• 10.4 Edge AI

Chapter 11. North America Market

• 11.1 Market Overview
• 11.2 Key Factors Impacting Market
  • 11.2.1 Market Drivers
  • 11.2.2 Market Restraints
  • 11.2.3 Market Opportunities
  • 11.2.4 Market Challenges
  • 11.2.5 Market Trends
  • 11.2.6 State of Competition
  • 11.2.7 Market Consolidation
  • 11.2.8 Key Customer Criteria
• 11.3 Product Life Cycle
• 11.4 Segmentation By Cooling Type
  • 11.4.1 Single-Phase
  • 11.4.2 Two-Phase
• 11.5 Segmentation By Cooling Level
  • 11.5.1 Component-Level Cooling
  • 11.5.2 Server Rack-Level Cooling
• 11.6 Segmentation By Power Density
  • 11.6.1 Below 300 W
  • 11.6.2 W-700 W
  • 11.6.3 Above 700 W
• 11.7 Segmentation By Deployment
  • 11.7.1 Hyperscale Cloud
  • 11.7.2 Enterprise
  • 11.7.3 Government and Research HPC
  • 11.7.4 Edge AI
• 11.8 Segmentation By Country
  • 11.8.1 US
    • 11.8.1.1 Segmentation By Cooling Type
      • 11.8.1.1.1 Single-Phase
      • 11.8.1.1.2 Two-Phase
    • 11.8.1.2 Segmentation By Cooling Level
      • 11.8.1.2.1 Component-Level Cooling
      • 11.8.1.2.2 Server Rack-Level Cooling
    • 11.8.1.3 Segmentation By Power Density
      • 11.8.1.3.1 Below 300 W
      • 11.8.1.3.2 W-700 W
      • 11.8.1.3.3 Above 700 W
    • 11.8.1.4 Segmentation By Deployment
      • 11.8.1.4.1 Hyperscale Cloud
      • 11.8.1.4.2 Enterprise
      • 11.8.1.4.3 Government and Research HPC
      • 11.8.1.4.4 Edge AI
  • 11.8.2 Canada
    • 11.8.2.1 Segmentation By Cooling Type
      • 11.8.2.1.1 Single-Phase
      • 11.8.2.1.2 Two-Phase
    • 11.8.2.2 Segmentation By Cooling Level
      • 11.8.2.2.1 Component-Level Cooling
      • 11.8.2.2.2 Server Rack-Level Cooling
    • 11.8.2.3 Segmentation By Power Density
      • 11.8.2.3.1 Below 300 W
      • 11.8.2.3.2 W-700 W
      • 11.8.2.3.3 Above 700 W
    • 11.8.2.4 Segmentation By Deployment
      • 11.8.2.4.1 Hyperscale Cloud
      • 11.8.2.4.2 Enterprise
      • 11.8.2.4.3 Government and Research HPC
      • 11.8.2.4.4 Edge AI
  • 11.8.3 Mexico
    • 11.8.3.1 Segmentation By Cooling Type
      • 11.8.3.1.1 Single-Phase
      • 11.8.3.1.2 Two-Phase
    • 11.8.3.2 Segmentation By Cooling Level
      • 11.8.3.2.1 Component-Level Cooling
      • 11.8.3.2.2 Server Rack-Level Cooling
    • 11.8.3.3 Segmentation By Power Density
      • 11.8.3.3.1 Below 300 W
      • 11.8.3.3.2 W-700 W
      • 11.8.3.3.3 Above 700 W
    • 11.8.3.4 Segmentation By Deployment
      • 11.8.3.4.1 Hyperscale Cloud
      • 11.8.3.4.2 Enterprise
      • 11.8.3.4.3 Government and Research HPC
      • 11.8.3.4.4 Edge AI
  • 11.8.4 Rest of North America
    • 11.8.4.1 Segmentation By Cooling Type
      • 11.8.4.1.1 Single-Phase
      • 11.8.4.1.2 Two-Phase
    • 11.8.4.2 Segmentation By Cooling Level
      • 11.8.4.2.1 Component-Level Cooling
      • 11.8.4.2.2 Server Rack-Level Cooling
    • 11.8.4.3 Segmentation By Power Density
      • 11.8.4.3.1 Below 300 W
      • 11.8.4.3.2 W-700 W
      • 11.8.4.3.3 Above 700 W
    • 11.8.4.4 Segmentation By Deployment
      • 11.8.4.4.1 Hyperscale Cloud
      • 11.8.4.4.2 Enterprise
      • 11.8.4.4.3 Government and Research HPC
      • 11.8.4.4.4 Edge AI

Chapter 12. Europe Market

• 12.1 Market Overview
• 12.2 Key Factors Impacting Market
  • 12.2.1 Market Drivers
  • 12.2.2 Market Restraints
  • 12.2.3 Market Opportunities
  • 12.2.4 Market Challenges
  • 12.2.5 Market Trends
  • 12.2.6 State of Competition
  • 12.2.7 Market Consolidation
  • 12.2.8 Key Customer Criteria
• 12.3 Product Life Cycle
• 12.4 Segmentation By Cooling Type
  • 12.4.1 Single-Phase
  • 12.4.2 Two-Phase
• 12.5 Segmentation By Cooling Level
  • 12.5.1 Component-Level Cooling
  • 12.5.2 Server Rack-Level Cooling
• 12.6 Segmentation By Power Density
  • 12.6.1 Below 300 W
  • 12.6.2 W-700 W
  • 12.6.3 Above 700 W
• 12.7 Segmentation By Deployment
  • 12.7.1 Hyperscale Cloud
  • 12.7.2 Enterprise
  • 12.7.3 Government and Research HPC
  • 12.7.4 Edge AI
• 12.8 Segmentation By Country
  • 12.8.1 Germany
    • 12.8.1.1 Segmentation By Cooling Type
      • 12.8.1.1.1 Single-Phase
      • 12.8.1.1.2 Two-Phase
    • 12.8.1.2 Segmentation By Cooling Level
      • 12.8.1.2.1 Component-Level Cooling
      • 12.8.1.2.2 Server Rack-Level Cooling
    • 12.8.1.3 Segmentation By Power Density
      • 12.8.1.3.1 Below 300 W
      • 12.8.1.3.2 W-700 W
      • 12.8.1.3.3 Above 700 W
    • 12.8.1.4 Segmentation By Deployment
      • 12.8.1.4.1 Hyperscale Cloud
      • 12.8.1.4.2 Enterprise
      • 12.8.1.4.3 Government and Research HPC
      • 12.8.1.4.4 Edge AI
  • 12.8.2 UK
    • 12.8.2.1 Segmentation By Cooling Type
      • 12.8.2.1.1 Single-Phase
      • 12.8.2.1.2 Two-Phase
    • 12.8.2.2 Segmentation By Cooling Level
      • 12.8.2.2.1 Component-Level Cooling
      • 12.8.2.2.2 Server Rack-Level Cooling
    • 12.8.2.3 Segmentation By Power Density
      • 12.8.2.3.1 Below 300 W
      • 12.8.2.3.2 W-700 W
      • 12.8.2.3.3 Above 700 W
    • 12.8.2.4 Segmentation By Deployment
      • 12.8.2.4.1 Hyperscale Cloud
      • 12.8.2.4.2 Enterprise
      • 12.8.2.4.3 Government and Research HPC
      • 12.8.2.4.4 Edge AI
  • 12.8.3 France
    • 12.8.3.1 Segmentation By Cooling Type
      • 12.8.3.1.1 Single-Phase
      • 12.8.3.1.2 Two-Phase
    • 12.8.3.2 Segmentation By Cooling Level
      • 12.8.3.2.1 Component-Level Cooling
      • 12.8.3.2.2 Server Rack-Level Cooling
    • 12.8.3.3 Segmentation By Power Density
      • 12.8.3.3.1 Below 300 W
      • 12.8.3.3.2 W-700 W
      • 12.8.3.3.3 Above 700 W
    • 12.8.3.4 Segmentation By Deployment
      • 12.8.3.4.1 Hyperscale Cloud
      • 12.8.3.4.2 Enterprise
      • 12.8.3.4.3 Government and Research HPC
      • 12.8.3.4.4 Edge AI
  • 12.8.4 Russia
    • 12.8.4.1 Segmentation By Cooling Type
      • 12.8.4.1.1 Single-Phase
      • 12.8.4.1.2 Two-Phase
    • 12.8.4.2 Segmentation By Cooling Level
      • 12.8.4.2.1 Component-Level Cooling
      • 12.8.4.2.2 Server Rack-Level Cooling
    • 12.8.4.3 Segmentation By Power Density
      • 12.8.4.3.1 Below 300 W
      • 12.8.4.3.2 W-700 W
      • 12.8.4.3.3 Above 700 W
    • 12.8.4.4 Segmentation By Deployment
      • 12.8.4.4.1 Hyperscale Cloud
      • 12.8.4.4.2 Enterprise
      • 12.8.4.4.3 Government and Research HPC
      • 12.8.4.4.4 Edge AI
  • 12.8.5 Spain
    • 12.8.5.1 Segmentation By Cooling Type
      • 12.8.5.1.1 Single-Phase
      • 12.8.5.1.2 Two-Phase
    • 12.8.5.2 Segmentation By Cooling Level
      • 12.8.5.2.1 Component-Level Cooling
      • 12.8.5.2.2 Server Rack-Level Cooling
    • 12.8.5.3 Segmentation By Power Density
      • 12.8.5.3.1 Below 300 W
      • 12.8.5.3.2 W-700 W
      • 12.8.5.3.3 Above 700 W
    • 12.8.5.4 Segmentation By Deployment
      • 12.8.5.4.1 Hyperscale Cloud
      • 12.8.5.4.2 Enterprise
      • 12.8.5.4.3 Government and Research HPC
      • 12.8.5.4.4 Edge AI
  • 12.8.6 Italy
    • 12.8.6.1 Segmentation By Cooling Type
      • 12.8.6.1.1 Single-Phase
      • 12.8.6.1.2 Two-Phase
    • 12.8.6.2 Segmentation By Cooling Level
      • 12.8.6.2.1 Component-Level Cooling
      • 12.8.6.2.2 Server Rack-Level Cooling
    • 12.8.6.3 Segmentation By Power Density
      • 12.8.6.3.1 Below 300 W
      • 12.8.6.3.2 W-700 W
      • 12.8.6.3.3 Above 700 W
    • 12.8.6.4 Segmentation By Deployment
      • 12.8.6.4.1 Hyperscale Cloud
      • 12.8.6.4.2 Enterprise
      • 12.8.6.4.3 Government and Research HPC
      • 12.8.6.4.4 Edge AI
  • 12.8.7 Rest of Europe
    • 12.8.7.1 Segmentation By Cooling Type
      • 12.8.7.1.1 Single-Phase
      • 12.8.7.1.2 Two-Phase
    • 12.8.7.2 Segmentation By Cooling Level
      • 12.8.7.2.1 Component-Level Cooling
      • 12.8.7.2.2 Server Rack-Level Cooling
    • 12.8.7.3 Segmentation By Power Density
      • 12.8.7.3.1 Below 300 W
      • 12.8.7.3.2 W-700 W
      • 12.8.7.3.3 Above 700 W
    • 12.8.7.4 Segmentation By Deployment
      • 12.8.7.4.1 Hyperscale Cloud
      • 12.8.7.4.2 Enterprise
      • 12.8.7.4.3 Government and Research HPC
      • 12.8.7.4.4 Edge AI

Chapter 13. Asia Pacific Market

• 13.1 Market Overview
• 13.2 Key Factors Impacting Market
  • 13.2.1 Market Drivers
  • 13.2.2 Market Restraints
  • 13.2.3 Market Opportunities
  • 13.2.4 Market Challenges
  • 13.2.5 Market Trends
  • 13.2.6 State of Competition
  • 13.2.7 Market Consolidation
  • 13.2.8 Key Customer Criteria
• 13.3 Product Life Cycle
• 13.4 Segmentation By Cooling Type
  • 13.4.1 Single-Phase
  • 13.4.2 Two-Phase
• 13.5 Segmentation By Cooling Level
  • 13.5.1 Component-Level Cooling
  • 13.5.2 Server Rack-Level Cooling
• 13.6 Segmentation By Power Density
  • 13.6.1 Below 300 W
  • 13.6.2 W-700 W
  • 13.6.3 Above 700 W
• 13.7 Segmentation By Deployment
  • 13.7.1 Hyperscale Cloud
  • 13.7.2 Enterprise
  • 13.7.3 Government and Research HPC
  • 13.7.4 Edge AI
• 13.8 Segmentation By Country
  • 13.8.1 China
    • 13.8.1.1 Segmentation By Cooling Type
      • 13.8.1.1.1 Single-Phase
      • 13.8.1.1.2 Two-Phase
    • 13.8.1.2 Segmentation By Cooling Level
      • 13.8.1.2.1 Component-Level Cooling
      • 13.8.1.2.2 Server Rack-Level Cooling
    • 13.8.1.3 Segmentation By Power Density
      • 13.8.1.3.1 Below 300 W
      • 13.8.1.3.2 W-700 W
      • 13.8.1.3.3 Above 700 W
    • 13.8.1.4 Segmentation By Deployment
      • 13.8.1.4.1 Hyperscale Cloud
      • 13.8.1.4.2 Enterprise
      • 13.8.1.4.3 Government and Research HPC
      • 13.8.1.4.4 Edge AI
  • 13.8.2 Japan
    • 13.8.2.1 Segmentation By Cooling Type
      • 13.8.2.1.1 Single-Phase
      • 13.8.2.1.2 Two-Phase
    • 13.8.2.2 Segmentation By Cooling Level
      • 13.8.2.2.1 Component-Level Cooling
      • 13.8.2.2.2 Server Rack-Level Cooling
    • 13.8.2.3 Segmentation By Power Density
      • 13.8.2.3.1 Below 300 W
      • 13.8.2.3.2 W-700 W
      • 13.8.2.3.3 Above 700 W
    • 13.8.2.4 Segmentation By Deployment
      • 13.8.2.4.1 Hyperscale Cloud
      • 13.8.2.4.2 Enterprise
      • 13.8.2.4.3 Government and Research HPC
      • 13.8.2.4.4 Edge AI
  • 13.8.3 India
    • 13.8.3.1 Segmentation By Cooling Type
      • 13.8.3.1.1 Single-Phase
      • 13.8.3.1.2 Two-Phase
    • 13.8.3.2 Segmentation By Cooling Level
      • 13.8.3.2.1 Component-Level Cooling
      • 13.8.3.2.2 Server Rack-Level Cooling
    • 13.8.3.3 Segmentation By Power Density
      • 13.8.3.3.1 Below 300 W
      • 13.8.3.3.2 W-700 W
      • 13.8.3.3.3 Above 700 W
    • 13.8.3.4 Segmentation By Deployment
      • 13.8.3.4.1 Hyperscale Cloud
      • 13.8.3.4.2 Enterprise
      • 13.8.3.4.3 Government and Research HPC
      • 13.8.3.4.4 Edge AI
  • 13.8.4 South Korea
    • 13.8.4.1 Segmentation By Cooling Type
      • 13.8.4.1.1 Single-Phase
      • 13.8.4.1.2 Two-Phase
    • 13.8.4.2 Segmentation By Cooling Level
      • 13.8.4.2.1 Component-Level Cooling
      • 13.8.4.2.2 Server Rack-Level Cooling
    • 13.8.4.3 Segmentation By Power Density
      • 13.8.4.3.1 Below 300 W
      • 13.8.4.3.2 W-700 W
      • 13.8.4.3.3 Above 700 W
    • 13.8.4.4 Segmentation By Deployment
      • 13.8.4.4.1 Hyperscale Cloud
      • 13.8.4.4.2 Enterprise
      • 13.8.4.4.3 Government and Research HPC
      • 13.8.4.4.4 Edge AI
  • 13.8.5 Singapore
    • 13.8.5.1 Segmentation By Cooling Type
      • 13.8.5.1.1 Single-Phase
      • 13.8.5.1.2 Two-Phase
    • 13.8.5.2 Segmentation By Cooling Level
      • 13.8.5.2.1 Component-Level Cooling
      • 13.8.5.2.2 Server Rack-Level Cooling
    • 13.8.5.3 Segmentation By Power Density
      • 13.8.5.3.1 Below 300 W
      • 13.8.5.3.2 W-700 W
      • 13.8.5.3.3 Above 700 W
    • 13.8.5.4 Segmentation By Deployment
      • 13.8.5.4.1 Hyperscale Cloud
      • 13.8.5.4.2 Enterprise
      • 13.8.5.4.3 Government and Research HPC
      • 13.8.5.4.4 Edge AI
  • 13.8.6 Taiwan
    • 13.8.6.1 Segmentation By Cooling Type
      • 13.8.6.1.1 Single-Phase
      • 13.8.6.1.2 Two-Phase
    • 13.8.6.2 Segmentation By Cooling Level
      • 13.8.6.2.1 Component-Level Cooling
      • 13.8.6.2.2 Server Rack-Level Cooling
    • 13.8.6.3 Segmentation By Power Density
      • 13.8.6.3.1 Below 300 W
      • 13.8.6.3.2 W-700 W
      • 13.8.6.3.3 Above 700 W
    • 13.8.6.4 Segmentation By Deployment
      • 13.8.6.4.1 Hyperscale Cloud
      • 13.8.6.4.2 Enterprise
      • 13.8.6.4.3 Government and Research HPC
      • 13.8.6.4.4 Edge AI
  • 13.8.7 Rest of Asia Pacific
    • 13.8.7.1 Segmentation By Cooling Type
      • 13.8.7.1.1 Single-Phase
      • 13.8.7.1.2 Two-Phase
    • 13.8.7.2 Segmentation By Cooling Level
      • 13.8.7.2.1 Component-Level Cooling
      • 13.8.7.2.2 Server Rack-Level Cooling
    • 13.8.7.3 Segmentation By Power Density
      • 13.8.7.3.1 Below 300 W
      • 13.8.7.3.2 W-700 W
      • 13.8.7.3.3 Above 700 W
    • 13.8.7.4 Segmentation By Deployment
      • 13.8.7.4.1 Hyperscale Cloud
      • 13.8.7.4.2 Enterprise
      • 13.8.7.4.3 Government and Research HPC
      • 13.8.7.4.4 Edge AI

Chapter 14. LAMEA Market

• 14.1 Market Overview
• 14.2 Key Factors Impacting Market
  • 14.2.1 Market Drivers
  • 14.2.2 Market Restraints
  • 14.2.3 Market Opportunities
  • 14.2.4 Market Challenges
  • 14.2.5 Market Trends
  • 14.2.6 State of Competition
  • 14.2.7 Market Consolidation
  • 14.2.8 Key Customer Criteria
• 14.3 Product Life Cycle
• 14.4 Segmentation By Cooling Type
  • 14.4.1 Single-Phase
  • 14.4.2 Two-Phase
• 14.5 Segmentation By Cooling Level
  • 14.5.1 Component-Level Cooling
  • 14.5.2 Server Rack-Level Cooling
• 14.6 Segmentation By Power Density
  • 14.6.1 Below 300 W
  • 14.6.2 W-700 W
  • 14.6.3 Above 700 W
• 14.7 Segmentation By Deployment
  • 14.7.1 Hyperscale Cloud
  • 14.7.2 Enterprise
  • 14.7.3 Government and Research HPC
  • 14.7.4 Edge AI
• 14.8 Segmentation By Country
  • 14.8.1 Brazil
    • 14.8.1.1 Segmentation By Cooling Type
      • 14.8.1.1.1 Single-Phase
      • 14.8.1.1.2 Two-Phase
    • 14.8.1.2 Segmentation By Cooling Level
      • 14.8.1.2.1 Component-Level Cooling
      • 14.8.1.2.2 Server Rack-Level Cooling
    • 14.8.1.3 Segmentation By Power Density
      • 14.8.1.3.1 Below 300 W
      • 14.8.1.3.2 W-700 W
      • 14.8.1.3.3 Above 700 W
    • 14.8.1.4 Segmentation By Deployment
      • 14.8.1.4.1 Hyperscale Cloud
      • 14.8.1.4.2 Enterprise
      • 14.8.1.4.3 Government and Research HPC
      • 14.8.1.4.4 Edge AI
  • 14.8.2 Argentina
    • 14.8.2.1 Segmentation By Cooling Type
      • 14.8.2.1.1 Single-Phase
      • 14.8.2.1.2 Two-Phase
    • 14.8.2.2 Segmentation By Cooling Level
      • 14.8.2.2.1 Component-Level Cooling
      • 14.8.2.2.2 Server Rack-Level Cooling
    • 14.8.2.3 Segmentation By Power Density
      • 14.8.2.3.1 Below 300 W
      • 14.8.2.3.2 W-700 W
      • 14.8.2.3.3 Above 700 W
    • 14.8.2.4 Segmentation By Deployment
      • 14.8.2.4.1 Hyperscale Cloud
      • 14.8.2.4.2 Enterprise
      • 14.8.2.4.3 Government and Research HPC
      • 14.8.2.4.4 Edge AI
  • 14.8.3 UAE
    • 14.8.3.1 Segmentation By Cooling Type
      • 14.8.3.1.1 Single-Phase
      • 14.8.3.1.2 Two-Phase
    • 14.8.3.2 Segmentation By Cooling Level
      • 14.8.3.2.1 Component-Level Cooling
      • 14.8.3.2.2 Server Rack-Level Cooling
    • 14.8.3.3 Segmentation By Power Density
      • 14.8.3.3.1 Below 300 W
      • 14.8.3.3.2 W-700 W
      • 14.8.3.3.3 Above 700 W
    • 14.8.3.4 Segmentation By Deployment
      • 14.8.3.4.1 Hyperscale Cloud
      • 14.8.3.4.2 Enterprise
      • 14.8.3.4.3 Government and Research HPC
      • 14.8.3.4.4 Edge AI
  • 14.8.4 Saudi Arabia
    • 14.8.4.1 Segmentation By Cooling Type
      • 14.8.4.1.1 Single-Phase
      • 14.8.4.1.2 Two-Phase
    • 14.8.4.2 Segmentation By Cooling Level
      • 14.8.4.2.1 Component-Level Cooling
      • 14.8.4.2.2 Server Rack-Level Cooling
    • 14.8.4.3 Segmentation By Power Density
      • 14.8.4.3.1 Below 300 W
      • 14.8.4.3.2 W-700 W
      • 14.8.4.3.3 Above 700 W
    • 14.8.4.4 Segmentation By Deployment
      • 14.8.4.4.1 Hyperscale Cloud
      • 14.8.4.4.2 Enterprise
      • 14.8.4.4.3 Government and Research HPC
      • 14.8.4.4.4 Edge AI
  • 14.8.5 South Africa
    • 14.8.5.1 Segmentation By Cooling Type
      • 14.8.5.1.1 Single-Phase
      • 14.8.5.1.2 Two-Phase
    • 14.8.5.2 Segmentation By Cooling Level
      • 14.8.5.2.1 Component-Level Cooling
      • 14.8.5.2.2 Server Rack-Level Cooling
    • 14.8.5.3 Segmentation By Power Density
      • 14.8.5.3.1 Below 300 W
      • 14.8.5.3.2 W-700 W
      • 14.8.5.3.3 Above 700 W
    • 14.8.5.4 Segmentation By Deployment
      • 14.8.5.4.1 Hyperscale Cloud
      • 14.8.5.4.2 Enterprise
      • 14.8.5.4.3 Government and Research HPC
      • 14.8.5.4.4 Edge AI
  • 14.8.6 Nigeria
    • 14.8.6.1 Segmentation By Cooling Type
      • 14.8.6.1.1 Single-Phase
      • 14.8.6.1.2 Two-Phase
    • 14.8.6.2 Segmentation By Cooling Level
      • 14.8.6.2.1 Component-Level Cooling
      • 14.8.6.2.2 Server Rack-Level Cooling
    • 14.8.6.3 Segmentation By Power Density
      • 14.8.6.3.1 Below 300 W
      • 14.8.6.3.2 W-700 W
      • 14.8.6.3.3 Above 700 W
    • 14.8.6.4 Segmentation By Deployment
      • 14.8.6.4.1 Hyperscale Cloud
      • 14.8.6.4.2 Enterprise
      • 14.8.6.4.3 Government and Research HPC
      • 14.8.6.4.4 Edge AI
  • 14.8.7 Rest of LAMEA
    • 14.8.7.1 Segmentation By Cooling Type
      • 14.8.7.1.1 Single-Phase
      • 14.8.7.1.2 Two-Phase
    • 14.8.7.2 Segmentation By Cooling Level
      • 14.8.7.2.1 Component-Level Cooling
      • 14.8.7.2.2 Server Rack-Level Cooling
    • 14.8.7.3 Segmentation By Power Density
      • 14.8.7.3.1 Below 300 W
      • 14.8.7.3.2 W-700 W
      • 14.8.7.3.3 Above 700 W
    • 14.8.7.4 Segmentation By Deployment
      • 14.8.7.4.1 Hyperscale Cloud
      • 14.8.7.4.2 Enterprise
      • 14.8.7.4.3 Government and Research HPC
      • 14.8.7.4.4 Edge AI

Chapter 15. Company Snapshots

• 15.1 Vertiv Holdings Co.
  • 15.1.1 Business Overview
  • 15.1.2 Key Information
  • 15.1.3 Company Focus on GPU Liquid Cooling Market
  • 15.1.4 Strategic Insights
  • 15.1.5 Strategy Deployed
  • 15.1.6 Product & Service Portfolio
  • 15.1.7 Capability Overview
  • 15.1.8 Technology & Innovation Focus
  • 15.1.9 SWOT Analysis
  • 15.1.10 Customers / End Users
  • 15.1.11 Competitive Positioning
  • 15.1.12 Key Differentiators
  • 15.1.13 Portfolio Matrix
  • 15.1.14 Future Outlook
  • 15.1.15 Analyst View
• 15.2 CoolIT Systems Inc.
  • 15.2.1 Business Overview
  • 15.2.2 Key Information
  • 15.2.3 Company Focus on GPU Liquid Cooling Market
  • 15.2.4 Strategic Insights
  • 15.2.5 Strategy Deployed
  • 15.2.6 Product & Service Portfolio
  • 15.2.7 Representative Products
  • 15.2.8 Capability Overview
  • 15.2.9 Technology & Innovation Focus
  • 15.2.10 SWOT Analysis
  • 15.2.11 Customers / End Users
  • 15.2.12 Competitive Positioning
  • 15.2.13 Key Differentiators
  • 15.2.14 Portfolio Matrix
  • 15.2.15 Analyst View
  • 15.2.16 Future Outlook
• 15.3 Schneider Electric SE
  • 15.3.1 Business Overview
  • 15.3.2 Key Information
  • 15.3.3 Company Focus on GPU Liquid Cooling Market
  • 15.3.4 Strategic Insights
  • 15.3.5 Strategy Deployed
  • 15.3.6 Product & Service Portfolio
  • 15.3.7 Representative Products
  • 15.3.8 Technology & Innovation Focus
  • 15.3.9 SWOT Analysis
  • 15.3.10 Customers / End Users
  • 15.3.11 Competitive Positioning
  • 15.3.12 Key Differentiators
  • 15.3.13 Portfolio Matrix
  • 15.3.14 Analyst View
  • 15.3.15 Future Outlook
• 15.4 Super Micro Computer, Inc.
  • 15.4.1 Business Overview
  • 15.4.2 Company Profile
  • 15.4.3 Company Focus on GPU Liquid Cooling Market
  • 15.4.4 Strategic Insights on GPU Liquid Cooling Market
  • 15.4.5 Strategy Deployed
  • 15.4.6 Product & Service Portfolio
  • 15.4.7 Representative GPU Liquid-Cooled Systems
  • 15.4.8 Technology & Innovation Focus
  • 15.4.9 SWOT Analysis
  • 15.4.10 Customers / End Users
  • 15.4.11 Competitive Positioning
  • 15.4.12 Key Differentiators
  • 15.4.13 Portfolio Matrix
  • 15.4.14 Analyst View
  • 15.4.15 Future Outlook
• 15.5 Boyd Corporation
  • 15.5.1 Business Overview
  • 15.5.2 Key Information
  • 15.5.3 Company Focus on GPU Liquid Cooling Market
  • 15.5.4 Strategic Insights
  • 15.5.5 Strategy Deployed
  • 15.5.6 Product & Service Portfolio
  • 15.5.7 Key Products / Services
  • 15.5.8 Capability Overview
  • 15.5.9 Technology & Innovation Focus
  • 15.5.10 SWOT Analysis
  • 15.5.11 Customers / End Users
  • 15.5.12 Competitive Positioning
  • 15.5.13 Key Differentiators
  • 15.5.14 Portfolio Matrix
  • 15.5.15 Analyst View
  • 15.5.16 Future Outlook
• 15.6 nVent Electric plc
  • 15.6.1 Business Overview
  • 15.6.2 Key Information
  • 15.6.3 Company Focus on GPU Liquid Cooling Market
  • 15.6.4 Strategic Insights
  • 15.6.5 Strategy Deployed
  • 15.6.6 Product & Service Portfolio
  • 15.6.7 Key Representative Products
  • 15.6.8 Capability Overview
  • 15.6.9 Technology & Innovation Focus
  • 15.6.10 SWOT Analysis
  • 15.6.11 Customers / End Users
  • 15.6.12 Competitive Positioning
  • 15.6.13 Key Differentiators
  • 15.6.14 Portfolio Matrix
  • 15.6.15 Analyst View
  • 15.6.16 Future Outlook
• 15.7 Delta Electronics, Inc.
  • 15.7.1 Business Overview
  • 15.7.2 Key Information
  • 15.7.3 Company Focus on GPU Liquid Cooling Market
  • 15.7.4 Strategic Insights
  • 15.7.5 Strategy Deployed
  • 15.7.6 Product & Service Portfolio
  • 15.7.7 Capability Overview
  • 15.7.8 Technology & Innovation Focus
  • 15.7.9 SWOT Analysis
  • 15.7.10 Customers / End Users
  • 15.7.11 Competitive Positioning
  • 15.7.12 Key Differentiators
  • 15.7.13 Portfolio Matrix
  • 15.7.14 Analyst View
  • 15.7.15 Future Outlook
• 15.8 Modine Manufacturing Company
  • 15.8.1 Business Overview
  • 15.8.2 Key Information
  • 15.8.3 Company Focus on GPU Liquid Cooling Market
  • 15.8.4 Strategic Insights
  • 15.8.5 Strategy Deployed
  • 15.8.6 Product & Service Portfolio
  • 15.8.7 Capability Overview
  • 15.8.8 Technology & Innovation Focus
  • 15.8.9 SWOT Analysis
  • 15.8.10 Customers / End Users
  • 15.8.11 Competitive Positioning
  • 15.8.12 Key Differentiators
  • 15.8.13 Portfolio Matrix
  • 15.8.14 Analyst View
  • 15.8.15 Future Outlook
• 15.9 LiquidStack
  • 15.9.1 Business Overview
  • 15.9.2 Key Information
  • 15.9.3 Company Focus on GPU Liquid Cooling Market
  • 15.9.4 Strategic Insights
  • 15.9.5 Strategy Deployed
  • 15.9.6 Product & Service Portfolio
  • 15.9.7 Capability Overview
  • 15.9.8 Technology & Innovation Focus
  • 15.9.9 SWOT Analysis
  • 15.9.10 Customers / End Users
  • 15.9.11 Competitive Positioning
  • 15.9.12 Key Differentiators
  • 15.9.13 Portfolio Matrix
  • 15.9.14 Analyst View
  • 15.9.15 Future Outlook
• 15.10 Rittal GmbH & Co. KG
  • 15.10.1 Business Overview
  • 15.10.2 Key Information
  • 15.10.3 Company Focus on GPU Liquid Cooling Market
  • 15.10.4 Strategic Insights
  • 15.10.5 Strategy Deployed
  • 15.10.6 Product & Service Portfolio
  • 15.10.7 Capability Overview
  • 15.10.8 Technology & Innovation Focus
  • 15.10.9 SWOT Analysis
  • 15.10.10 Customers / End Users
  • 15.10.11 Competitive Positioning
  • 15.10.12 Key Differentiators
  • 15.10.13 Portfolio Matrix
  • 15.10.14 Analyst View
  • 15.10.15 Future Outlook

Chapter 16. Winning Imperatives of GPU Liquid Cooling Market