Advanced and Emerging Technology Market Research
You are at:»»Computing and AI for Data Centers: Global Market 2027–2040
Global Computing and AI for Data Centers Market Report 2026-2040

Computing and AI for Data Centers: Global Market 2027–2040

0

Computing and AI for Data Centers: Global Market 2027–2040: Data centers are the physical infrastructure of the AI era — and they are consuming capital and energy at a rate that is reshaping national power grids. The computing AI data center market covers the full stack of hardware, infrastructure, and services that AI training and inference workloads demand: from the AI accelerator silicon and high bandwidth memory at the chip level, through the advanced packaging, cooling, and power delivery systems at the module and rack level, to the data centre construction, power supply, and interconnect infrastructure at the facility level. The computing AI data center market is not a single product category but a value chain that begins at the transistor and ends at the building — and every layer of that chain is growing faster than the overall digital economy.

The computing AI data center market is being driven by a convergence of demand signals that are independently powerful and mutually reinforcing. Foundation model training requires clusters of tens of thousands of GPUs running continuously at rack power densities that conventional data centre cooling cannot handle. AI inference at hyperscale serving billions of daily requests requires the most efficient accelerator and memory architectures the industry can produce. And the emergence of sovereign AI programmes, neocloud operators, and enterprise AI infrastructure build-outs is broadening the customer base well beyond the original five hyperscalers.

Computing and AI for Data Centers Market Report 2027-2040 — Key Coverage Areas

  • AI Accelerator Silicon — NVIDIA GPU roadmap through Blackwell Ultra and Rubin; AMD Instinct MI400 series; Google TPU Trillium and successors; Amazon Trainium 3; custom ASIC landscape; Chinese AI chip ecosystem under export controls; and AI accelerator market forecast by vendor tier through 2040
  • High Bandwidth Memory and the AI Memory Stack — HBM3E, HBM4, and HBM4E technology roadmaps; SK Hynix, Samsung, and Micron production capacity and technology leadership; CXL memory pooling for inference; and the AI memory market forecast
  • Advanced Packaging for AI Infrastructure — TSMC CoWoS and SoIC; Intel Foveros and EMIB; glass core substrates; ABF substrate supply; and the advanced packaging capacity constraint on AI accelerator production
  • Data Centre Power Infrastructure — the transition to 48V and 800V HVDC rack architectures; SiC and GaN power semiconductors for AI server PSUs; voltage regulator modules; and the data centre power delivery market
  • Cooling for AI Data Centres — direct-to-chip liquid cooling; rear-door heat exchangers; single-phase and two-phase immersion cooling; the PFAS-free fluorocarbon cooling fluid transition; and the data centre cooling infrastructure market forecast
  • Data Centre Networking — InfiniBand and Ethernet AI cluster networking; NVIDIA Quantum-X800 and Spectrum-X800; Broadcom Tomahawk 6; and the networking infrastructure market for AI data centres
  • Co-Packaged Optics for Data Centres — scale-out CPO for switch ASICs; scale-up CPO for GPU-to-GPU interconnect; the managed transition from pluggable to co-packaged architectures; and the CPO adoption timeline
  • Data Centre Construction and Real Estate — the hyperscaler capital expenditure programme; campus development; power procurement including nuclear PPAs; and the data centre real estate investment market
  • AI Cloud Services Market — hyperscaler and neocloud AI compute service revenue; GPU-as-a-service market; and enterprise AI infrastructure investment
  • 14-Year Forecasts — computing AI data center market value by infrastructure layer, customer tier, and region from 2027 through 2040

The computing AI data center market report is the definitive intelligence resource for AI infrastructure investors, semiconductor companies, data centre operators, and enterprise technology strategists.

Ideal for AI infrastructure investors, semiconductor companies, data centre operators, cloud service providers, and enterprise technology strategists.

cover

cover

Semiconductors, AI Chips & Accelerators, Data Center Processors, AI Infrastructure, Advanced Packaging & Memory, Semiconductor Supply Chain

  • Published: September 2026
  • Pages: 329
  • Tables: 117
  • Figures: 133

 

The market for computing and AI silicon in data centers covers the processors that do the work inside AI and cloud infrastructure: discrete GPUs, custom AI ASICs, server CPUs and data center FPGAs. What has changed since 2023 is not simply scale but structure. The server CPU, which accounted for the clear majority of this market in 2021, now represents a small fraction of it, while the GPU has moved from a minority position to dominance — the fastest reversal of category leadership in semiconductor history.

Recent activity has been defined by three developments. Custom silicon has moved from experiment to volume: Google's TPU, AWS Trainium, Meta's MTIA and Microsoft's MAIA together now ship millions of accelerators annually, and OpenAI's own programme is expected in volume from 2027. Merchant vendors have responded by selling systems rather than chips, with rack-scale platforms integrating 72 to 144 accelerators behind a single coherent fabric — a shift that raises the barrier to competing from designing a chip to delivering an entire rack, along with its power delivery, liquid cooling and system software. And the binding constraint has migrated from silicon to electricity: after packaging shortages in 2023 and high-bandwidth memory shortages through 2024 and 2025, grid interconnection and electrical equipment lead times now govern how quickly capacity can be commissioned.

The outlook to 2040 is for continued growth at a materially slower rate than the 2023–2027 period. Three findings shape that trajectory. Revenue keeps growing after units stop: accelerator shipments peak around 2032 while average selling prices rise more than five-fold across the period, meaning capacity sized against the revenue curve will be overbuilt. AI ASICs overtake GPUs on unit shipments in 2028 but never on revenue, because custom silicon displaces volume at the lower-value inference end while frontier training remains merchant territory. And the cost reductions driving demand come predominantly from model efficiency and serving software rather than from process scaling — meaning much of the value created accrues above the silicon layer.

Value in this chain is determined by position more than by execution. The constrained layers — leading-edge foundry, high-bandwidth memory, advanced packaging and custom silicon co-design — combine growth with genuine defensibility, while system assembly and the merchant accelerator start-up cohort face structurally weaker economics. Risks are concentrated rather than diffuse: power availability, packaging capacity, memory supply, and whether enterprise AI adoption converts from pilot to production at the rate the buildout assumes.

Computing and AI for Data Centers: Global Market 2027–2040 is a comprehensive market intelligence report on the semiconductors powering global AI and cloud infrastructure. The report covers chip designers, foundries, memory suppliers, packaging houses, equipment vendors, hyperscalers, model developers, systems manufacturers and infrastructure suppliers across the United States, Taiwan, South Korea, Japan, China and Europe.

Report contents include: 

  • Executive summary and market definition
  • Global AI infrastructure investment and hyperscaler capital expenditure
  • Data center power constraints and regional capacity buildout
  • Export controls and the US–China technology divide
  • Processor market revenue forecasts to 2040: GPU, AI ASIC, server CPU, FPGA
  • Average selling price and unit volume forecasts by vendor and programme
  • Wafer, die and advanced packaging demand forecasts
  • Server tray and rack architecture forecasts
  • Cost of AI inference and training, and the token cost roadmap
  • Demand drivers: agentic AI, physical AI, recommendation, coding, search
  • Capital expenditure versus operating expenditure economics
  • Ecosystem, supply chain and co-designer relationship maps
  • Market share by revenue, units and deployment volume
  • Financial analysis of the leading chip designers
  • AI semiconductor start-up funding landscape
  • Mainland China market, manufacturers and supply chain
  • CPU, GPU and AI ASIC technology and roadmap analysis
  • HBM, advanced packaging and rack bill of materials
  • Emerging architectures: photonics, neuromorphic, quantum
  • Bull, base and bear scenarios to 2040, risks and investment outlook
  • 81 company profiles. Companies profiled include Advanced Micro Devices (AMD), Alchip Technologies, Alibaba (T-Head Semiconductor), Alphawave Semi, Amazon Web Services (Annapurna Labs), Amkor Technology, Ampere Computing, Analog Devices, Applied Materials, Arm Holdings, ASE Technology Holding, ASML Holding, Astera Labs, Axelera AI, Baidu (Kunlun), Biren Technology, Broadcom, Cadence Design Systems, Cambricon Technologies, Celestica, Cerebras Systems, Cisco Systems, Coherent Corp, CoreWeave, Credo Technology Group, ChangXin Memory Technologies (CXMT), d-Matrix, Delta Electronics, Dell Technologies, Eaton Corporation, Enflame Technology, Etched, Foxconn (Hon Hai Precision Industry), FuriosaAI, GlobalFoundries, Google (Alphabet), Groq, Global Unichip Corporation (GUC), Hewlett Packard Enterprise, Hygon Information Technology, Ibiden, Iluvatar CoreX, Infineon Technologies, Innolight Technology, Intel Corporation, JCET Group, KLA Corporation, Lam Research, Lightmatter, Lumentum Holdings, Marvell Technology, MediaTek, Meta Platforms, Micron Technology, Microsoft, Monolithic Power Systems, Moore Threads, Nebius Group, NVIDIA Corporation, onsemi, OpenAI, Powertech Technology, Quanta Computer, Rambus, Rebellions, Renesas Electronics, Samsung Electronics, SambaNova Systems, Schneider Electric, Shinko Electric Industries, SiPearl, SK Hynix, Semiconductor Manufacturing International Corporation (SMIC), Super Micro Computer, Synopsys, Tenstorrent, Tokyo Electron, Taiwan Semiconductor Manufacturing Company (TSMC), Unimicron Technology, Vertiv Holdings, and Wistron Corporation.

 

 

1             EXECUTIVE SUMMARY            19

  • 1.1        Global AI Infrastructure and Investment Landscape           19
  • 1.2        Capital formation and the venture channel               21
  • 1.3        US and Chinese Hyperscaler CapEx Trends and Projections         23
  • 1.4        Power as the binding constraint        24
  • 1.5        AI Data Center Typology and Demand Segmentation          27
  • 1.6        AI Regulatory Landscape and Export Controls        29
  • 1.7        Sovereign AI and International Industrial Policy      30
  • 1.8        The US–China Technology Divide     33

 

2             MARKET FORECASTS                35

  • 2.1        Processor Revenue Forecast               35
    • 2.1.1    Total Data Center Processor Market, 2021–2040  35
    • 2.1.2    GPU Revenue Forecast, 2021–2040 (p. 64) 39
    • 2.1.3    AI ASIC Revenue Forecast, 2021–2040 (p. 68)         44
    • 2.1.4    Server CPU Revenue Forecast, 2021–2040 (p. 71) 46
    • 2.1.5    FPGA Data Center Revenue Forecast, 2021–2040 48
  • 2.2        Average Selling Price (ASP) Forecast              49
    • 2.2.1    GPU ASP Trends by Product Tier, 2021–2040           50
    • 2.2.2    AI ASIC ASP Trends by Hyperscaler, 2021–2040     52
    • 2.2.3    CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 (p. 84) 54
  • 2.3        Processor Volume Forecast 55
    • 2.3.1    GPU Unit Shipments by Vendor, 2021–2040             55
    • 2.3.2    AI ASIC Unit Shipments by Hyperscaler, 2021–2040           59
    • 2.3.3    CPU Unit Shipments by Vendor, 2021–2040             63
  • 2.4        Wafer Forecast             67
    • 2.4.1    GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040      67
    • 2.4.2    Wafer Starts by Foundry         70
    • 2.4.3    GPU & AI ASIC Compute Die Forecast, 2021–2040              72
    • 2.4.4    HBM-Driven Revenue Separation from GPU & AI ASIC        74
  • 2.5        Server Tray Volume Forecast               76
  • 2.6        AI server rack architecture     78
  • 2.7        CPU Focus       79
  • 2.8        The Arm ramp                81
  • 2.9        GPU & AI ASIC Focus 84

 

3             MARKET TRENDS        89

  • 3.1        Cost of Generative AI Inference and Training            89
  • 3.2        Why training costs what it does         91
  • 3.3        From Agentic AI to Physical AI             95
  • 3.4        Physical AI       98
  • 3.5        Recommendation Models for Social Networks       100
  • 3.6        Coding Assistants      103
  • 3.7        Search Engine vs. LLM             107
  • 3.8        OpenClaw        110
    • 3.8.1    What it is           110
    • 3.8.2    Why it matters to this forecast           110
  • 3.9        CapEx vs. OpEx in the Era of Generative AI 111
  • 3.10     Is the Future of AI Data Centers in Space? 116

 

4             MARKET SHARE & SUPPLY CHAIN    120

  • 4.1        Data Center Ecosystem Map               120
  • 4.2        Foundation Models Ecosystem Map              124
  • 4.3        U.S. vs. China Tech War — Timeline                128
  • 4.4        Financial Metrics of Data Center Chip Designers  132
  • 4.5        AI semiconductor start-up fundraising        137
  • 4.6        Case Study: OpenAI Revenue and Gigawatt              140
  • 4.7        Market Share: CPU, GPU, AI ASIC & XPU Co-Designers     144
    • 4.7.1    GPU Market Share by Revenue and Units (p. 247) 144
    • 4.7.2    AI ASIC Market Share by Hyperscaler             147
    • 4.7.3    CPU Market Share by Vendor               149
    • 4.7.4    XPU Co-Designer Revenue Market Share    151
  • 4.8        Focus on China            153

 

5             TECHNOLOGY ANALYSIS       161

  • 5.1        CPU Technology Trends          161
    • 5.1.1    x86 Architecture Evolution    162
    • 5.1.2    Arm-Based CPU Momentum in the Data Center     164
    • 5.1.3    RISC-V in the Data Center (p. 289)   165
    • 5.1.4    CPU Specialization for AI Workloads             166
  • 5.2        GPU Technology Trends          167
  • Process Node Roadmap and Transition       167
    • 5.2.1    Chiplet and Multi-Die Architectures               169
    • 5.2.2    Rack-Scale GPU Architectures — NVL72 and Beyond        170
    • 5.2.3    Memory Bandwidth and HBM Integration   172
    • 5.2.4    Networking and Interconnect Evolution       173
  • 5.3        AI Data Center Traffic Patterns and Network Architecture                174
    • 5.3.1    Four tiers, four different problems   175
    • 5.3.2    Why the traffic looks the way it does              176
  • 5.4        AI ASIC Technology Trends    177
    • 5.4.1    Hyperscaler ASIC Product Roadmaps          177
    • 5.4.2    AI ASIC Start-Up Landscape 178
    • 5.4.3    AI ASIC Technology Specification Database             179
    • 5.4.4    Compute Disaggregation for AI Inference    182
    • 5.4.5    KV Cache and the Storage Tier           182
  • 5.5        GPU vs. AI ASIC: Comparative Analysis       184
  • 5.6        Advanced Packaging and HBM Memory      188
    • 5.6.1    HBM Technology Roadmap — HBM2E to HBM4     188
    • 5.6.2    CoWoS and Advanced Packaging Capacity               190
    • 5.6.3    Custom HBM and Co-Design Trends              193
    • 5.6.4    AI Rack Bill of Materials           194
    • 5.6.5    Thermal Management and Cooling Technologies  196
  • 5.7        Emerging Computing Architectures                200
    • 5.7.1    Photonic Computing 200
    • 5.7.2    Neuromorphic Computing   202
    • 5.7.3    Quantum Computing Outlook            203

 

6             OUTLOOK & SCENARIOS       205

  • 6.1        Market Outlook 2026–2040 205
  • 6.2        Technology Outlook 2026–2040       208
  • 6.3        Key Risks and Opportunities                209
  • 6.4        Strategic Recommendations               211

 

7             COMPANY PROFILES                213 (81 company profiles)

 

8             REPORT METHODOLOGY     315

  • 8.1        Objective of the report             315
  • 8.2        Scope of this report   315

 

9             GLOSSARY OF TERMS AND ABBREVIATIONS           316

 

10          REFERENCES 324

 

List of Tables

  • Table 1. Global AI Infrastructure Investment by Category, 2021–2040 ($B)          20
  • Table 2. US Early-Stage Venture Deployment in AI and Adjacent Technologies, 2016–2026 YTD           21
  • Table 3. Early-stage round size and investor concentration, 2026 YTD    22
  • Table 4. US vs. Chinese Hyperscaler CapEx, 2021–2040 ($B)       24
  • Table 5. Data Center Installed IT Load by Workload Type, 2024–2040 (GW)        25
  • Table 6. AI Data Center Facility Types: Characteristics and Processor Preferences        27
  • Table 7. Data Center Processor Demand by Facility Type, 2021–2040 ($B)          28
  • Table 8. Sovereign AI and Semiconductor Programs           31
  • Table 9. Sovereign and Government AI Processor Demand by Region, 2025–2040 ($B)              32
  • Table 10. Data Center Processor Market Revenue Summary, 2021–2040 ($B)   36
  • Table 11. Revenue Breakdown by Processor Type (CPU, GPU, AI ASIC, FPGA), 2021–2040       37
  • Table 12. GPU Revenue by Vendor, 2021–2040 ($B)             40
  • Table 13. Nvidia GPU Revenue by Product Generation, 2021–2028 ($B) 42
  • Table 14. AMD GPU Revenue by Product Generation, 2021–2028 ($B)    43
  • Table 15. AI ASIC Revenue by Hyperscaler, 2021–2040 ($B)           45
  • Table 16. Server CPU Revenue by Vendor, 2021–2040 ($B)             47
  • Table 17. FPGA Data Center Revenue Forecast, 2021–2040 ($M)               49
  • Table 18. GPU ASP by Product Tier, 2021–2040 ($K per unit)          50
  • Table 19. AI ASIC ASP by Hyperscaler, 2021–2040 ($K per unit)    52
  • Table 20. Server CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 ($)   54
  • Table 21. GPU Unit Shipments by Vendor, 2021–2040 (K units)    55
  • Table 22. Nvidia GPU Unit Shipments by Product Generation, 2021–2028 (K units)        57
  • Table 23. AMD GPU Unit Shipments by Product Generation, 2021–2028 (K units)           58
  • Table 24. AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units)  59
  • Table 25. CPU Unit Shipments by Vendor, 2021–2040 (M units)   64
  • Table 26. Hyperscaler Custom CPU Unit Adoption, 2022–2040 (M units)             65
  • Table 27. GPU & AI ASIC Wafer Starts by Node and Foundry, 2021–2040 (KWPM, 300mm equivalent)                68
  • Table 28. Average Die Size Trend — GPU vs. AI ASIC, 2021–2040 (mm²) 73
  • Table 29. HBM Revenue Separated from GPU & AI ASIC Total, 2021–2040 ($B) 75
  • Table 30. AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units)         76
  • Table 31. AI Server Rack Configuration and Architecture, 2025–2040     78
  • Table 32. CPU Market Share by Revenue: Intel vs. AMD vs. Arm-based, 2021–2040      80
  • Table 33. Hyperscaler Arm CPU Deployment Ramp, 2022–2040 81
  • Table 34. CPU Processor Roadmap Summary — Major Vendors, 2024–2030    82
  • Table 35.GPU Market Share by Revenue, 2021–2040 (%)  84
  • Table 36. AI ASIC Market Share by Deployment Volume, 2021–2040 (%)               85
  • Table 37. GPU & AI ASIC Split by Technology Node, 2021–2040   86
  • Table 38. GPU & AI ASIC Product Roadmap Summary, 2024–2030           87
  • Table 39. Cost per Token Trend: Training and Inference, 2021–2040 ($/M tokens)            90
  • Table 40. Cost per Token by Model Size and Hardware Configuration, 2024–2040 ($/M output tokens)                90
  • Table 41. Cost per Token Trend: Training and Inference, 2021–2040 ($/M tokens)            91
  • Table 42. Inference Cost Breakdown by Infrastructure Component, 2025 (%)   92
  • Table 43. AI Model Parameter Count vs. Hardware Requirements, 2020–2028 94
  • Table 44. Agentic AI Use Cases by Industry and Hardware Requirements             96
  • Table 45. AI Agent Deployment Forecast by Sector, 2025–2040 (M concurrently deployed agents)     97
  • Table 46. Physical AI Hardware Requirements vs. Generative AI, 2025–2040     98
  • Table 47. Robotics Semiconductor Market Forecast, 2024–2040 ($B)    100
  • Table 48. Recommendation Model Architecture Evolution, 2018–2028 101
  • Table 49. Recommendation Model Compute Demand by Platform, 2024–2040 ($B)    102
  • Table 50. AI-Powered Coding Assistant Market Share, 2024–2028 (%)    104
  • Table 51. Coding Assistant Market Share and Underlying Infrastructure 105
  • Table 52. Coding AI GPU Compute Demand, 2024–2040 ($B)      106
  • Table 53. LLM vs. Traditional Search: Query Volume Forecast, 2022–2040 (B queries/day)       107
  • Table 54. AI Search Compute Infrastructure Requirements, 2024–2040 109
  • Table 55. CapEx Cycle: US Hyperscalers, 2015–2040 ($B)              112
  • Table 56. CapEx-to-Revenue Ratio: Major Hyperscalers, 2020–2040 (%)              113
  • Table 57. AI Infrastructure OpEx vs. CapEx Split, 2024–2040        114
  • Table 58. Cloud AI Chip Rental vs. Ownership Economics, 2025–2040  116
  • Table 59. Low Earth Orbit Latency and Bandwidth Projections, 2025–2035        118
  • Table 60. AI Chip Supply Chain: From Silicon to Hyperscaler         121
  • Table 61. Foundation Model Training Infrastructure by Developer               127
  • Table 62. Chinese AI Chip Import Replacement Progress, 2022–2028 (%)           129
  • Table 63. Sanctioned vs. Unsanctioned Chinese AI Chip Revenues, 2022–2028 ($B)   131
  • Table 64. Financial Metrics: Top 10 Data Center Chip Designers, 2021–2025    132
  • Table 65. Gross Margin Comparison: Nvidia vs. AMD vs. Intel, 2020–2025 (%)  134
  • Table 66. R&D Spend as % of Revenue: Key Chip Designers, 2020–2025              135
  • Table 67. US and Chinese Hyperscaler CapEx Summary, 2021–2026 ($B)           136
  • Table 68. AI Semiconductor Start-Up Fundraising, 2019–Q1 2026 ($M) 137
  • Table 69. AI Semiconductor Start-Up Fundraising Database, 2019–Q1 2026     138
  • Table 70. OpenAI Revenue Forecast, 2023–2030 ($B)        141
  • Table 71. OpenAI Compute Demand (Gigawatt), 2023–2030        142
  • Table 72. OpenAI GPU Procurement Forecast by Generation, 2023–2028 (K units)        143
  • Table 73. GPU Market Share Summary by Revenue and Units, 2021–2025          145
  • Table 74. Nvidia, AMD, Google, AWS GPU/ASIC Unit Split, 2021–2028 (K units) 147
  • Table 75. AI ASIC Market Share by Hyperscaler, 2021–2025 (%)  148
  • Table 76. AI ASIC Specifications: Google, AWS, Microsoft, Meta, 2024–2026    148
  • Table 77. CPU Market Share by Revenue: Intel vs. AMD vs. Arm, 2021–2025 (%)              150
  • Table 78. Hyperscaler Custom CPU Market Share Evolution, 2022–2028             150
  • Table 79. Co-Designer Revenue Share, 2021–2026 (%)     152
  • Table 80. China Data Center Processor Market by Type, 2021–2030 ($B)              154
  • Table 81. Chinese Hyperscaler Processor Demand, 2021–2028 ($B)       155
  • Table 82. Domestic Chinese AI Chip Makers: Unit Share, 2022–2028 (%)             156
  • Table 83. Chinese AI Chip Makers: Product Specifications and Capabilities      157
  • Table 84. China Data Center Semiconductor Supply Chain Map 159
  • Table 85. CPU Architecture Comparison: x86, Arm, RISC-V for the Data Center               161
  • Table 86. CPU Specifications: Intel, AMD, AWS, Google, Microsoft, Huawei, Nvidia, 2024–2026         162
  • Table 87. Arm Server CPU Shipment Forecast, 2022–2040 (M units)        164
  • Table 88. RISC-V Data Center Adoption Forecast, 2025–2040      165
  • Table 89. CPU Specialization for AI Inference Workloads 167
  • Table 90. GPU Specifications: Nvidia Blackwell, Rubin; AMD MI350X, MI450, 2024–2026        168
  • Table 91. GPU Die Size Evolution and Chiplet Transition, 2020–2030 (mm²)       169
  • Table 92. Rack-Scale GPU Architecture: NVL72 and Next-Generation Platforms             170
  • Table 93. GPU Memory Bandwidth Trend: HBM Generations, 2020–2030 (TB/s)              172
  • Table 94. NVLink and Interconnect Bandwidth Evolution, 2020–2030    174
  • Table 95. AI Network Tiers: Bandwidth, Latency and Technology 175
  • Table 96. Parallelism Strategies and Their Network Traffic Characteristics           176
  • Table 97. AI ASIC Technology Specification Database        180
  • Table 98. Memory and Storage Tiers for KV Cache in Inference Serving  183
  • Table 99. GPU vs. AI ASIC: Performance per Watt Comparison, 2022–2026       184
  • Table 100. GPU vs. AI ASIC: Training vs. Inference Suitability Matrix          185
  • Table 101. GPU vs. AI ASIC: Total Cost of Ownership Analysis      187
  • Table 102. HBM Specification Comparison: HBM2E, HBM3, HBM3E, HBM4      189
  • Table 103. CoWoS Capacity Expansion Roadmap: TSMC, 2022–2028 (KWPM) 191
  • Table 104. Advanced Packaging Market Share: CoWoS, SoIC, Others, 2024–2028 (%) 192
  • Table 105. AI Server Rack BoM: Itemized Cost Breakdown, 2025 ($K)     194
  • Table 106. AI Rack BoM Cost Evolution, 2023–2028 ($K)  195
  • Table 107. Data Center Cooling Technologies for AI: Comparison             196
  • Table 108. Cooling Technology Share of AI Accelerator Deployments, 2023–2040 (%) 199
  • Table 109. Silicon Photonics Market Forecast in Data Centers, 2024–2040 ($B)              200
  • Table 110. Neuromorphic Computing Roadmap, 2024–2040       202
  • Table 111. Quantum Computing Timeline to Commercial Viability, 2025–2040 203
  • Table 112. Emerging Computing Technology Readiness Assessment      204
  • Table 113. Market Forecast Summary: Bull / Base / Bear Scenarios, 2026–2040 ($B)   205
  • Table 114. Bull, Base, Bear Case Revenue Scenarios by Processor Type, 2040 207
  • Table 115. Technology Roadmap Summary: CPU, GPU, AI ASIC, 2026–2040     208
  • Table 116. Key Risk Register: Probability and Impact Assessment             210
  • Table 117. Investment Opportunity Map: Data Center Semiconductor Ecosystem        211

 

List of Figures

  • Figure 1. Global AI Infrastructure Investment Forecast, 2021–2040 ($B) 20
  • Figure 2. US vs. Chinese Hyperscaler CapEx, 2021–2040 ($B)     23
  • Figure 3. Data Center Power Consumption Forecast, 2024–2040 (GW installed IT load)             25
  • Figure 4. AI-Related Data Center Construction Starts by Region, 2022–2028 (GW of IT capacity)         26
  • Figure 5. Data Center Processor Demand by Facility Type, 2021–2040 ($B)        28
  • Figure 6. US Export Controls on AI Chips: Key Milestones, 2019–2026   30
  • Figure 7. Sovereign and Government AI Processor Demand by Region, 2025–2040 ($B)             32
  • Figure 8. US–China Technology Decoupling Timeline, 2018–2026             33
  • Figure 9. Total Data Center Processor Market Revenue Forecast, 2021–2040 ($B)         35
  • Figure 10. Revenue Breakdown by Processor Type (CPU, GPU, AI ASIC, FPGA), 2021–2040     37
  • Figure 11. Data Center Processor CAGR by Category, 2025–2040 (%)     38
  • Figure 12. GPU Market Revenue Forecast, 2021–2040 ($B)            39
  • Figure 13. GPU Revenue Split by Vendor (Nvidia, AMD, Others), 2021–2040       40
  • Figure 14. Nvidia GPU Revenue by Product Generation, 2021–2028 ($B)              42
  • Figure 15. AMD GPU Revenue by Product Generation, 2021–2028 ($B)  43
  • Figure 16. AI ASIC Market Revenue Forecast, 2021–2040 ($B)      44
  • Figure 17. AI ASIC Revenue Split by Hyperscaler, 2021–2040        45
  • Figure 18. Server CPU Market Revenue Forecast, 2021–2040 ($B)            46
  • Figure 19. Server CPU Revenue Split by Architecture (x86 vs. Arm), 2021–2040 47
  • Figure 20. FPGA Data Center Revenue Forecast, 2021–2040 ($M)             48
  • Figure 21. GPU ASP Evolution by Product Tier, 2021–2040 ($K)    50
  • Figure 22. AI ASIC ASP Trends by Hyperscaler, 2021–2040 ($K)   52
  • Figure 23. Server CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 ($) 54
  • Figure 24. GPU Unit Shipments by Vendor, 2021–2040 (K units)  55
  • Figure 25. Nvidia GPU Unit Shipments by Product Generation, 2021–2028 (K units)      57
  • Figure 26. AMD GPU Unit Shipments by Product Generation, 2021–2028 (K units)         58
  • Figure 27. AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units) 59
  • Figure 28. Google TPU Unit Deployment Forecast, 2021–2040 (K units) 61
  • Figure 29. AWS Trainium & Inferentia Unit Forecast, 2021–2040 (K units)              62
  • Figure 30. Microsoft MAIA Unit Forecast, 2021–2040 (K units)      63
  • Figure 31. CPU Unit Shipments — Data Center, 2021–2040 (M units)      63
  • Figure 32. Intel vs. AMD CPU Market Share in Unit Terms, 2021–2040 (%)            64
  • Figure 33. Hyperscaler Custom CPU Unit Adoption, 2022–2040 (M units)            65
  • Figure 34. GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040 (KWPM)            67
  • Figure 35. Wafer Consumption Split: Advanced Nodes, 2021–2040         68
  • Figure 36. GPU & AI ASIC Wafer Starts by Foundry, 2021–2040    70
  • Figure 37. TSMC Advanced Node Capacity Forecast, 2024–2040 (KWPM)           71
  • Figure 38. GPU & AI ASIC Compute Die Forecast, 2021–2040      72
  • Figure 39. Average Die Size Trend — GPU vs. AI ASIC, 2021–2040 (mm²)               73
  • Figure 40. HBM Revenue Separated from GPU & AI ASIC Total, 2021–2040 ($B)               74
  • Figure 41. AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units)       76
  • Figure 42. AI Server Rack Configuration and Architecture, 2025–2040   78
  • Figure 43. CPU Market Share by Revenue: Intel vs. AMD vs. Arm-based, 2021–2040    80
  • Figure 44. Hyperscaler Arm CPU Deployment Ramp, 2022–2040              81
  • Figure 45. CPU Product Roadmap: Intel, AMD, Arm, Google, AWS, Nvidia, 2024–2030               82
  • Figure 46. GPU Market Share by Revenue, 2021–2040 (%)               84
  • Figure 47. AI ASIC Market Share by Deployment Volume, 2021–2040 (%)             85
  • Figure 48. GPU & AI ASIC Split by Technology Node, 2021–2040 86
  • Figure 49. Cost per Token Trend: Training and Inference, 2021–2040 ($/M tokens)          89
  • Figure 50. Training Compute Requirements by Model Type, 2020–2028 (FLOPs)              91
  • Figure 51. Inference Cost Breakdown by Infrastructure Component, 2025 (%)  92
  • Figure 52. Token Cost Reduction Roadmap, 2025–2040  93
  • Figure 53. AI Model Parameter Count vs. Hardware Requirements, 2020–2028 94
  • Figure 54. Agentic AI Market Taxonomy and Use Cases     95
  • Figure 55. AI Agent Deployment Forecast by Sector, 2025–2040 (M concurrently deployed agents)   97
  • Figure 56. Physical AI Hardware Requirements vs. Generative AI, 2025–2040   98
  • Figure 57. Robotics Semiconductor Market Forecast, 2024–2040 ($B)  99
  • Figure 58. Recommendation Model Architecture Evolution, 2018–2028 101
  • Figure 59. Recommendation Model Compute Demand by Platform, 2024–2040 ($B)  102
  • Figure 60. AI-Powered Coding Assistant Market Share, 2024–2028 (%)  104
  • Figure 61. Coding AI GPU Compute Demand, 2024–2040 ($B)    106
  • Figure 62. LLM vs. Traditional Search: Query Volume Forecast, 2022–2040 (B queries/day)     107
  • Figure 63. AI Search Compute Infrastructure Requirements, 2024–2040             109
  • Figure 64. CapEx Cycle: US Hyperscalers, 2015–2040 ($B)            112
  • Figure 65. CapEx-to-Revenue Ratio: Major Hyperscalers, 2020–2040 (%)            113
  • Figure 66. AI Infrastructure OpEx vs. CapEx Split, 2024–2040      114
  • Figure 67. Cloud AI Chip Rental vs. Ownership Economics, 2025–2040 115
  • Figure 68. Space-Based Data Center Conceptual Architecture    117
  • Figure 69. Low Earth Orbit Latency and Bandwidth Projections, 2025–2035      118
  • Figure 70. Global Data Center Processor Ecosystem Map               120
  • Figure 71. AI Chip Supply Chain: From Silicon to Hyperscaler Source: Future Markets, Inc., 2026      121
  • Figure 72. Co-Designer and Hyperscaler Relationship Map           122
  • Figure 73. OSAT and Advanced Packaging Supply Chain Map       123
  • Figure 74. Foundation Models Ecosystem Map: Developers and Infrastructure                124
  • Figure 75. Open vs. Closed Source AI Model Landscape, 2024   126
  • Figure 76. Foundation Model Training Infrastructure by Developer             127
  • Figure 77. US Export Control Timeline: Semiconductors, 2018–2026      128
  • Figure 78. Chinese AI Chip Import Replacement Progress, 2022–2028 (%)         129
  • Figure 79. Sanctioned vs. Unsanctioned Chinese AI Chip Revenues, 2022–2028 ($B) 131
  • Figure 80. Comparative Revenue: Data Center Chip Designers, 2021–2025 ($B)             132
  • Figure 81. Gross Margin Comparison: Nvidia vs. AMD vs. Intel, 2020–2025 (%) 134
  • Figure 82. R&D Spend as % of Revenue: Key Chip Designers, 2020–2025            135
  • Figure 83. AI Semiconductor Start-Up Fundraising, 2019–Q1 2026 ($M)               137
  • Figure 84. OpenAI Revenue Forecast, 2023–2030 ($B)      140
  • Figure 85. OpenAI Compute Demand (Gigawatt), 2023–2030      141
  • Figure 86. OpenAI GPU Procurement Forecast by Generation, 2023–2028 (K units)      143
  • Figure 87. GPU Market Share by Revenue, 2021–2025 (%)               144
  • Figure 88. GPU Market Share by Units, 2021–2025 (%)      145
  • Figure 89. Nvidia, AMD, Google, AWS GPU/ASIC Unit Split, 2021–2028 (K units)              146
  • Figure 90. AI ASIC Market Share by Hyperscaler, 2021–2025 (%) 147
  • Figure 91. CPU Market Share by Revenue: Intel vs. AMD vs. Arm, 2021–2025 (%)            149
  • Figure 92. Hyperscaler Custom CPU Market Share Evolution, 2022–2028           150
  • Figure 93. XPU Co-Designer Revenue, 2023–2026 ($B)     151
  • Figure 94. XPU Co-Designer Revenue Share, 2021–2026 (%)         152
  • Figure 95. China Data Center Processor Market Size, 2021–2025 ($B)    154
  • Figure 96. Chinese Hyperscaler Processor Demand, 2021–2028 ($B)     155
  • Figure 97. Domestic Chinese AI Chip Makers: Unit Share, 2022–2028 (%)           156
  • Figure 98. HiSilicon, Cambricon, Baidu Kunlun, Hygon Roadmap, 2024–2028 158
  • Figure 99. China Data Center Semiconductor Supply Chain Map              159
  • Figure 100. CPU Architecture Comparison: x86, Arm, RISC-V for the Data Center           161
  • Figure 101. Arm Server CPU Shipment Forecast, 2022–2040 (M units)   164
  • Figure 102. RISC-V Data Center Adoption Forecast, 2025–2040 165
  • Figure 103. CPU Specialization for AI Inference Workloads            166
  • Figure 104. GPU Process Node Roadmap: Nvidia, AMD, 2020–2030       167
  • Figure 105. GPU Die Size Evolution and Chiplet Transition, 2020–2030 (mm²)  169
  • Figure 106. Rack-Scale GPU Architecture: NVL72 and Next-Generation Platforms        170
  • Figure 107. GPU Memory Bandwidth Trend: HBM Generations, 2020–2030 (TB/s)          172
  • Figure 108. NVLink and Interconnect Bandwidth Evolution, 2020–2030 173
  • Figure 109. AI Data Center Network Architecture by Tier   175
  • Figure 110. Hyperscaler ASIC Roadmap Comparison: Google, AWS, Microsoft, Meta  177
  • Figure 111. AI ASIC Start-Up Landscape by Funding Stage, 2024               178
  • Figure 112. AI ASIC Technology Specification Matrix           179
  • Figure 113. Disaggregated Inference Architecture Diagram            182
  • Figure 114. GPU vs. AI ASIC: Performance per Watt Comparison, 2022–2026   184
  • Figure 115. GPU vs. AI ASIC: Training vs. Inference Suitability Matrix        185
  • Figure 116. GPU vs. AI ASIC: Total Cost of Ownership Analysis    186
  • Figure 117. HBM Technology Roadmap: HBM2E to HBM4, 2020–2028   188
  • Figure 118. HBM Bandwidth and Capacity per Stack by Generation, 2020–2028            189
  • Figure 119. CoWoS Capacity Expansion Roadmap: TSMC, 2022–2028 (KWPM)               190
  • Figure 120. Advanced Packaging Market Share: CoWoS, SoIC, Others, 2024–2028 (%)              192
  • Figure 121. Custom HBM Co-Design Relationships Map  193
  • Figure 122. AI Server Rack Bill of Materials: Component Breakdown, 2025 ($K)               194
  • Figure 123. AI Rack BoM Cost Evolution, 2023–2028 ($K) 195
  • Figure 124. Rack Density Envelope by Cooling Technology             198
  • Figure 125. Cooling Technology Share of AI Accelerator Deployments, 2023–2040 (%)               199
  • Figure 126. Silicon Photonics Market Forecast in Data Centers, 2024–2040 ($B)            200
  • Figure 127. Neuromorphic Computing Roadmap, 2024–2040     202
  • Figure 128. Quantum Computing Timeline to Commercial Viability, 2025–2040             203
  • Figure 129. Data Center Processor Market Scenario Analysis, 2026–2040 ($B) 205
  • Figure 130. Bull, Base, Bear Case Revenue Scenarios by Processor Type, 2040               207
  • Figure 131. Technology Roadmap Summary: CPU, GPU, AI ASIC, 2026–2040   208
  • Figure 132. Competitive Landscape Risk Matrix, 2026–2040        209
  • Figure 133. Investment Opportunity Map: Data Center Semiconductor Ecosystem       211

 

 

 

 

Purchasers will receive the following:

  • PDF report download/by email. 
  • Comprehensive Excel spreadsheet of all data.
  • Mid-year Update

 

Computing and AI for Data Centers: Global Market 2027–2040
Computing and AI for Data Centers: Global Market 2027–2040
PDF download.
Price: £1,100.00

Computing and AI for Data Centers: Global Market 2027–2040
Computing and AI for Data Centers: Global Market 2027–2040
PDF and Print Edition (including tracked delivery).
Price: £1,200.00

 

Convert these prices to your currency:

 

 

Payment methods: Visa, Mastercard, American Express, Bank Transfer. To order by Bank Transfer (Invoice) select this option from the payment methods menu after adding to cart, or contact info@futuremarketsinc.com

 

Name
Updates
We'll use your details to send the sample you requested.

Related Posts