The global AI servers market was valued at USD 285.0 billion in 2025. It is estimated at USD 395.0 billion in 2026 and is forecast to reach USD 1.07 trillion by 2036, growing at a CAGR of 10.5% over the 2026-2036 period. This report examines the market in depth, covering hyperscaler and sovereign AI investment, GPU and custom accelerator platforms, high-bandwidth memory and advanced packaging, interconnects, liquid cooling and power delivery, the ODM and OEM supply chain, power and energy needs, export controls and tariffs, pricing, the value chain, competition, and future growth opportunities.
The study covers servers and integrated rack-scale systems built to train and run AI models. Each pairs GPUs, custom ASICs, or other accelerators with host CPUs, high-bandwidth memory, interconnects, power, and cooling. Coverage spans NVIDIA GB200 and GB300 NVL72 racks, eight-way and similar servers using NVIDIA HGX, AMD Instinct, or Intel Gaudi parts, systems designed around hyperscaler chips like Google TPU and Amazon Trainium, and inference servers for enterprise and edge sites. The market is valued at the system level, so it includes the accelerators and other components inside each server. Separately sold networking and storage equipment, data center buildings, and cloud services fall outside the scope.
Spending on AI compute has reached record levels, as NVIDIA reported fiscal 2026 revenue of USD 215.9 billion, up 65%, and according to its finance chief, analysts expected the five largest cloud and hyperscale companies to spend close to USD 700 billion on capital projects in 2026. Dell Technologies reported AI server orders worth USD 64 billion for fiscal 2026 and a year-end backlog of USD 43 billion. Energy is now a limiting factor: according to the International Energy Agency, data center electricity use was about 415 terawatt-hours in 2024 and could reach around 945 terawatt-hours by 2030.
Power and grid capacity are the main restraint, as rack-scale systems concentrate far more power per rack than older servers and many new sites face multi-year waits for grid connections. Component supply is also tight: TSMC has doubled CoWoS packaging capacity in both 2024 and 2025, yet demand still exceeds supply, and Samsung raised some memory chip prices by up to 60% in December 2025. U.S. export controls have removed most of the China opportunity for U.S. accelerators, and NVIDIA reported that sales of its China-specific H20 chip fell to roughly USD 50 million in one quarter of fiscal 2026. Server makers also earn thin margins, because accelerators make up most of each system's value. Challenges include the complexity of building and commissioning liquid-cooled racks, dependence on a few large buyers, uncertain returns on AI spending, annual platform cycles from Hopper to Blackwell, Blackwell Ultra, and Rubin, and the concentration of assembly and packaging in Taiwan.
Liquid-cooled rack-scale systems offer the largest opportunity, since vendors that ship fully integrated and tested racks capture more value per system; Supermicro reported design wins above USD 12 billion requested for delivery in its second fiscal quarter of 2026. Servers based on custom accelerators are another opportunity, as Amazon reported that its chip business, including Trainium, had reached a USD 20 billion annual run rate. Enterprise and edge inference opens a broad market as companies run models on their own data for cost, latency, security, and sovereignty reasons. Key trends include the move to rack-scale architectures, diversification into custom silicon from Google, Amazon, Meta, and Microsoft, liquid cooling as a standard feature, and a rising share of inference workloads.
The processor type analysis covers servers built on GPUs, on custom ASICs, and on FPGAs and other accelerators. GPU-based servers are expected to hold the largest share in 2026, reflecting the strength of NVIDIA's data center platforms, the mature CUDA software ecosystem that most AI developers rely on, and the wide availability of GPU systems from both ODMs and OEMs, with AMD Instinct offering a second GPU option. Custom ASIC-based servers are expected to grow at the highest CAGR during the forecast period, as hyperscalers expand in-house chips such as TPUs, Trainium, MTIA, and Maia to lower cost per workload and reduce reliance on a single supplier, supported by design partners such as Broadcom and Marvell.
By form factor, the study covers rack-scale systems, multi-accelerator servers, and edge & inference servers. Multi-accelerator servers are expected to account for the largest share in 2026, given the large installed base of eight-GPU HGX-class servers and their continued deployment across cloud and enterprise data centers that can house them without major facility changes. Rack-scale systems are expected to grow at the highest CAGR, driven by the ramp of GB300 NVL72, which links 72 GPUs and 36 Grace CPUs in one NVLink domain, and by future platforms that place even more accelerators in a single rack.
By cooling technology, the market is divided into air-cooled and liquid-cooled servers, with liquid cooling further split into direct-to-chip and immersion. Air-cooled servers are expected to hold the larger share in 2026, as HGX-class and enterprise AI servers continue to be installed in existing data halls built for air. Liquid-cooled servers are expected to register the higher CAGR, as rack-scale platforms such as GB200 and GB300 NVL72 require direct-to-chip cooling and accelerator power keeps rising beyond what air can remove.
By workload, the report covers training and inference. Training is expected to account for the larger share in 2026, as hyperscalers and frontier model developers continue to build very large clusters to develop new models, and these clusters use the most expensive rack-scale systems. Inference is expected to grow at the higher CAGR, as AI applications reach hundreds of millions of users and reasoning and agentic models multiply the compute used per request. Inference also favors a wider hardware mix, with more focus on cost per token.
By end user, the study covers hyperscalers, neoclouds & tier-2 cloud providers, enterprises (BFSI, healthcare & life sciences, manufacturing & automotive, telecommunications, and other enterprises), sovereign & government, and research & education. Hyperscalers are expected to hold the largest share in 2026, given the scale of their capital budgets and their position as the main buyers of both merchant GPU systems and custom accelerator servers. Sovereign & government buyers are expected to grow at the highest CAGR, as countries in the Gulf, Europe, and Asia-Pacific fund national AI compute, from Stargate UAE to the EU's planned AI gigafactories.
By vendor type, the report covers original design manufacturers (ODMs) and original equipment manufacturers (OEMs). ODMs are expected to account for the larger share in 2026, since hyperscalers source most of their racks straight from Taiwan-based builders like Foxconn, Quanta, and Wistron, which produce to customer designs at very high volume. OEMs are expected to register the higher CAGR, as neoclouds, enterprises, and sovereign customers prefer the validated systems, services, support, and financing offered by vendors such as Dell, HPE, Supermicro, and Lenovo, a shift visible in Dell's growing AI server backlog.
North America is expected to remain the largest regional market in 2026, since the leading hyperscalers, the main AI model developers, and most accelerator and server companies are based in the U.S., where the Stargate project of OpenAI, SoftBank, and Oracle, announced in January 2025, plans investment of up to USD 500 billion according to its sponsors. NVIDIA also plans domestic AI system production with Foxconn in Houston and Wistron in Fort Worth.
Asia-Pacific is expected to grow at the highest CAGR during the forecast period, led by Taiwan, the global center of AI server production through Foxconn, Quanta, Wistron, Wiwynn, and Inventec and TSMC's advanced packaging. NVIDIA said in October 2025 it would ship roughly 260,000 GPUs for Korean public and private projects, Japan is adding AI supercomputers, and India's IndiaAI Mission subsidizes access to thousands of GPUs. China, meanwhile, is shifting to servers that use local chips like Ascend from Huawei. In Europe, the European Commission's InvestAI plan targets EUR 200 billion of AI investment, of which EUR 20 billion is earmarked for gigafactories, and France, Germany, the U.K., and the Nordic countries attract most new capacity. In the Middle East & Africa, Gulf sovereign programs lead growth, while Brazil, Chile, and Mexico lead Latin America, with Mexico also emerging as an AI server production base.
Accelerator supply is concentrated, with NVIDIA in the lead and AMD, Intel, and hyperscaler custom silicon programs as alternatives, while system manufacturing is split between Taiwanese ODMs serving hyperscalers and global OEMs serving enterprises, neoclouds, and governments; Chinese vendors supply the domestic market with local accelerators. Vendors compete on accelerator allocation, speed of platform transitions, rack-scale and liquid cooling capability, manufacturing capacity and location, services and financing, and price, and the leaders are putting money into integrated rack production, liquid cooling, and new U.S. and Mexican plants, including Foxconn's expansion in Guadalajara.
Key companies profiled in the report include NVIDIA Corporation (U.S.), Advanced Micro Devices, Inc. (U.S.), Intel Corporation (U.S.), Broadcom Inc. (U.S.), Dell Technologies Inc. (U.S.), Hewlett Packard Enterprise Company (U.S.), Super Micro Computer, Inc. (U.S.), Lenovo Group Limited (China), Cisco Systems, Inc. (U.S.), Fujitsu Limited (Japan), Hon Hai Precision Industry Co., Ltd. (Foxconn) (Taiwan), Quanta Computer Inc. (Taiwan), Wistron Corporation (Taiwan), Wiwynn Corporation (Taiwan), Inventec Corporation (Taiwan), GIGA-BYTE Technology Co., Ltd. (Taiwan), ASUSTeK Computer Inc. (Taiwan), Celestica Inc. (Canada), Inspur Electronic Information Industry Co., Ltd. (China), and Huawei Technologies Co., Ltd. (China).
The study covers servers and integrated rack-scale systems built to train and run AI models. Each pairs GPUs, custom ASICs, or other accelerators with host CPUs, high-bandwidth memory, interconnects, power, and cooling. Coverage spans NVIDIA GB200 and GB300 NVL72 racks, eight-way and similar servers using NVIDIA HGX, AMD Instinct, or Intel Gaudi parts, systems designed around hyperscaler chips like Google TPU and Amazon Trainium, and inference servers for enterprise and edge sites. The market is valued at the system level, so it includes the accelerators and other components inside each server. Separately sold networking and storage equipment, data center buildings, and cloud services fall outside the scope.
Spending on AI compute has reached record levels, as NVIDIA reported fiscal 2026 revenue of USD 215.9 billion, up 65%, and according to its finance chief, analysts expected the five largest cloud and hyperscale companies to spend close to USD 700 billion on capital projects in 2026. Dell Technologies reported AI server orders worth USD 64 billion for fiscal 2026 and a year-end backlog of USD 43 billion. Energy is now a limiting factor: according to the International Energy Agency, data center electricity use was about 415 terawatt-hours in 2024 and could reach around 945 terawatt-hours by 2030.
Market Dynamics
Hyperscaler capital budgets are the single biggest demand driver, and according to company guidance, Meta alone plans USD 125 billion to USD 145 billion of capital spending in 2026, much of it for AI servers. Generative and agentic AI adds to demand for both training and inference, as reasoning models need more computation per query; Google reported that its Gemini API handles over 16 billion tokens every minute. Demand is also spreading beyond the largest cloud companies to neoclouds, enterprises, and governments, with the UAE and the U.S. announcing a 1-gigawatt Stargate UAE cluster in May 2025 and Saudi Arabia's Humain agreeing to buy 18,000 NVIDIA GB300 chips.Power and grid capacity are the main restraint, as rack-scale systems concentrate far more power per rack than older servers and many new sites face multi-year waits for grid connections. Component supply is also tight: TSMC has doubled CoWoS packaging capacity in both 2024 and 2025, yet demand still exceeds supply, and Samsung raised some memory chip prices by up to 60% in December 2025. U.S. export controls have removed most of the China opportunity for U.S. accelerators, and NVIDIA reported that sales of its China-specific H20 chip fell to roughly USD 50 million in one quarter of fiscal 2026. Server makers also earn thin margins, because accelerators make up most of each system's value. Challenges include the complexity of building and commissioning liquid-cooled racks, dependence on a few large buyers, uncertain returns on AI spending, annual platform cycles from Hopper to Blackwell, Blackwell Ultra, and Rubin, and the concentration of assembly and packaging in Taiwan.
Liquid-cooled rack-scale systems offer the largest opportunity, since vendors that ship fully integrated and tested racks capture more value per system; Supermicro reported design wins above USD 12 billion requested for delivery in its second fiscal quarter of 2026. Servers based on custom accelerators are another opportunity, as Amazon reported that its chip business, including Trainium, had reached a USD 20 billion annual run rate. Enterprise and edge inference opens a broad market as companies run models on their own data for cost, latency, security, and sovereignty reasons. Key trends include the move to rack-scale architectures, diversification into custom silicon from Google, Amazon, Meta, and Microsoft, liquid cooling as a standard feature, and a rising share of inference workloads.
Segment Analysis
The report analyzes the market by processor type, form factor, cooling technology, workload, end user, vendor type, and geography, helping stakeholders see where AI server spending is concentrated and how it is shifting.The processor type analysis covers servers built on GPUs, on custom ASICs, and on FPGAs and other accelerators. GPU-based servers are expected to hold the largest share in 2026, reflecting the strength of NVIDIA's data center platforms, the mature CUDA software ecosystem that most AI developers rely on, and the wide availability of GPU systems from both ODMs and OEMs, with AMD Instinct offering a second GPU option. Custom ASIC-based servers are expected to grow at the highest CAGR during the forecast period, as hyperscalers expand in-house chips such as TPUs, Trainium, MTIA, and Maia to lower cost per workload and reduce reliance on a single supplier, supported by design partners such as Broadcom and Marvell.
By form factor, the study covers rack-scale systems, multi-accelerator servers, and edge & inference servers. Multi-accelerator servers are expected to account for the largest share in 2026, given the large installed base of eight-GPU HGX-class servers and their continued deployment across cloud and enterprise data centers that can house them without major facility changes. Rack-scale systems are expected to grow at the highest CAGR, driven by the ramp of GB300 NVL72, which links 72 GPUs and 36 Grace CPUs in one NVLink domain, and by future platforms that place even more accelerators in a single rack.
By cooling technology, the market is divided into air-cooled and liquid-cooled servers, with liquid cooling further split into direct-to-chip and immersion. Air-cooled servers are expected to hold the larger share in 2026, as HGX-class and enterprise AI servers continue to be installed in existing data halls built for air. Liquid-cooled servers are expected to register the higher CAGR, as rack-scale platforms such as GB200 and GB300 NVL72 require direct-to-chip cooling and accelerator power keeps rising beyond what air can remove.
By workload, the report covers training and inference. Training is expected to account for the larger share in 2026, as hyperscalers and frontier model developers continue to build very large clusters to develop new models, and these clusters use the most expensive rack-scale systems. Inference is expected to grow at the higher CAGR, as AI applications reach hundreds of millions of users and reasoning and agentic models multiply the compute used per request. Inference also favors a wider hardware mix, with more focus on cost per token.
By end user, the study covers hyperscalers, neoclouds & tier-2 cloud providers, enterprises (BFSI, healthcare & life sciences, manufacturing & automotive, telecommunications, and other enterprises), sovereign & government, and research & education. Hyperscalers are expected to hold the largest share in 2026, given the scale of their capital budgets and their position as the main buyers of both merchant GPU systems and custom accelerator servers. Sovereign & government buyers are expected to grow at the highest CAGR, as countries in the Gulf, Europe, and Asia-Pacific fund national AI compute, from Stargate UAE to the EU's planned AI gigafactories.
By vendor type, the report covers original design manufacturers (ODMs) and original equipment manufacturers (OEMs). ODMs are expected to account for the larger share in 2026, since hyperscalers source most of their racks straight from Taiwan-based builders like Foxconn, Quanta, and Wistron, which produce to customer designs at very high volume. OEMs are expected to register the higher CAGR, as neoclouds, enterprises, and sovereign customers prefer the validated systems, services, support, and financing offered by vendors such as Dell, HPE, Supermicro, and Lenovo, a shift visible in Dell's growing AI server backlog.
Regional Analysis
Geographic coverage includes North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa, with analysis of 21 countries, the Nordic countries, Singapore & Southeast Asia, and five rest-of-region groups. The regional analysis considers hyperscaler and sovereign investment, AI server manufacturing, power availability, and the effect of export controls and tariffs in each market.North America is expected to remain the largest regional market in 2026, since the leading hyperscalers, the main AI model developers, and most accelerator and server companies are based in the U.S., where the Stargate project of OpenAI, SoftBank, and Oracle, announced in January 2025, plans investment of up to USD 500 billion according to its sponsors. NVIDIA also plans domestic AI system production with Foxconn in Houston and Wistron in Fort Worth.
Asia-Pacific is expected to grow at the highest CAGR during the forecast period, led by Taiwan, the global center of AI server production through Foxconn, Quanta, Wistron, Wiwynn, and Inventec and TSMC's advanced packaging. NVIDIA said in October 2025 it would ship roughly 260,000 GPUs for Korean public and private projects, Japan is adding AI supercomputers, and India's IndiaAI Mission subsidizes access to thousands of GPUs. China, meanwhile, is shifting to servers that use local chips like Ascend from Huawei. In Europe, the European Commission's InvestAI plan targets EUR 200 billion of AI investment, of which EUR 20 billion is earmarked for gigafactories, and France, Germany, the U.K., and the Nordic countries attract most new capacity. In the Middle East & Africa, Gulf sovereign programs lead growth, while Brazil, Chile, and Mexico lead Latin America, with Mexico also emerging as an AI server production base.
Competitive Landscape
The report reviews key growth strategies, competitive benchmarking, and the competitive dashboard, which places companies among industry leaders, market differentiators, vanguards, and emerging companies, together with a 2025 market share/rank analysis. Profiles are grouped into accelerator and silicon suppliers, server OEMs, ODMs and manufacturing partners, and China-focused vendors, and cover business overview, financial overview, product portfolio, strategic developments, and SWOT analysis.Accelerator supply is concentrated, with NVIDIA in the lead and AMD, Intel, and hyperscaler custom silicon programs as alternatives, while system manufacturing is split between Taiwanese ODMs serving hyperscalers and global OEMs serving enterprises, neoclouds, and governments; Chinese vendors supply the domestic market with local accelerators. Vendors compete on accelerator allocation, speed of platform transitions, rack-scale and liquid cooling capability, manufacturing capacity and location, services and financing, and price, and the leaders are putting money into integrated rack production, liquid cooling, and new U.S. and Mexican plants, including Foxconn's expansion in Guadalajara.
Key companies profiled in the report include NVIDIA Corporation (U.S.), Advanced Micro Devices, Inc. (U.S.), Intel Corporation (U.S.), Broadcom Inc. (U.S.), Dell Technologies Inc. (U.S.), Hewlett Packard Enterprise Company (U.S.), Super Micro Computer, Inc. (U.S.), Lenovo Group Limited (China), Cisco Systems, Inc. (U.S.), Fujitsu Limited (Japan), Hon Hai Precision Industry Co., Ltd. (Foxconn) (Taiwan), Quanta Computer Inc. (Taiwan), Wistron Corporation (Taiwan), Wiwynn Corporation (Taiwan), Inventec Corporation (Taiwan), GIGA-BYTE Technology Co., Ltd. (Taiwan), ASUSTeK Computer Inc. (Taiwan), Celestica Inc. (Canada), Inspur Electronic Information Industry Co., Ltd. (China), and Huawei Technologies Co., Ltd. (China).
How This Report Helps
- Provides reliable market size estimates and long-term forecasts for the global AI servers market through 2036.
- Explains how hyperscaler capital spending, generative and agentic AI, and sovereign AI programs are shaping server demand.
- Assesses the impact of power and grid limits, memory and packaging shortages, export controls, and thin server margins on the market.
- Identifies high-growth opportunities across processor type, form factor, cooling technology, workload, end user, vendor type, and region.
- Tracks technology shifts such as rack-scale architectures, custom accelerators, direct-to-chip liquid cooling, and scale-up interconnects.
- Benchmarks accelerator suppliers, server OEMs, and ODMs on product platforms, manufacturing footprint, and strategy.
- Supports product planning, capacity investment, sourcing, partnership, and market entry decisions for server makers, component suppliers, data center operators, and investors.
Key Questions Answered
- How large is the global AI servers market today, and how will it grow through 2036?
- How much of hyperscaler capital spending is flowing into AI servers, and how durable is this demand?
- What are the main drivers, restraints, opportunities, challenges, and trends shaping the market?
- How quickly will custom ASIC-based servers and rack-scale systems gain ground against conventional GPU servers?
- How are power availability, liquid cooling, and memory and packaging supply affecting deployment?
- Which processor type, form factor, workload, end user, and vendor type segments will grow the fastest?
- Which regions and countries offer the most attractive opportunities, and how are export controls reshaping demand?
- Who are the leading accelerator suppliers, OEMs, and ODMs, and how do they compare?
- What manufacturing investments, partnerships, and product launches are shaping competition?
Table of Contents
1. Introduction
2. Research Methodology
4. Market Overview
5. AI Servers Market, by Processor Type
6. AI Servers Market, by Form Factor
7. AI Servers Market, by Cooling Technology
8. AI Servers Market, by Workload
9. AI Servers Market, by End User
10. AI Servers Market, by Vendor Type
11. AI Servers Market, by Geography
12. Competitive Landscape
13. Company Profiles
14. Appendix
LIST OF TABLES
LIST OF FIGURES
Companies Mentioned
- NVIDIA Corporation
- Advanced Micro Devices, Inc.
- Intel Corporation
- Broadcom Inc.
- Dell Technologies Inc.
- Hewlett Packard Enterprise Company
- Super Micro Computer, Inc.
- Lenovo Group Limited
- Cisco Systems, Inc.
- Fujitsu Limited
- Hon Hai Precision Industry Co., Ltd.
- Quanta Computer Inc.
- Wistron Corporation
- Wiwynn Corporation
- Inventec Corporation
- GIGA-BYTE Technology Co., Ltd.
- ASUSTeK Computer Inc.
- Celestica Inc.
- Inspur Electronic Information Industry Co., Ltd.
- Huawei Technologies Co., Ltd.
