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The Global Market for Computing and AI for Data Centers 2026–2040

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  • Published: April 2026
  • Pages: 490
  • Tables: 32
  • Figures: 82

 

The global market for computing and artificial intelligence in data centers represents one of the most dynamic and capital-intensive segments of the semiconductor industry. Driven by the rapid proliferation of generative AI, large language models, and agentic AI systems, demand for specialised data center processors — encompassing GPUs, AI ASICs, CPUs, and FPGAs — has entered a period of extraordinary and sustained growth. From a market valued at approximately $215 billion in 2025, the sector is projected to scale dramatically through 2040, as hyperscalers, cloud providers, and enterprises race to build the compute infrastructure required to train, fine-tune, and serve increasingly powerful AI models.

At the core of this expansion is the GPU, which remains the dominant processor architecture for AI workloads due to its unmatched parallel processing capability and mature software ecosystem. Nvidia continues to hold an overwhelming share of this segment, with successive generations — from Hopper to Blackwell to Rubin and beyond — each delivering step-change improvements in compute density, memory bandwidth, and energy efficiency. AMD provides meaningful competition with its MI-series accelerators, while the broader landscape is being reshaped by hyperscalers developing their own custom silicon to reduce dependency on merchant chip vendors and lower total cost of ownership.

AI ASICs represent the fastest-growing processor category, as companies including Google, Amazon Web Services, Microsoft, and Meta invest heavily in purpose-built chips optimised for specific workloads such as inference, recommendation, and training. These internally developed accelerators — including Google's TPU series, AWS Trainium and Inferentia, Microsoft MAIA, and Meta's MTIA — are increasingly displacing third-party GPUs for certain use cases, fundamentally altering the competitive dynamics of the market and creating a parallel ecosystem of chip co-designers and advanced packaging specialists.

The server CPU market, though more mature, continues to evolve rapidly. Intel and AMD maintain leading positions with their x86 architectures, but face mounting pressure from Arm-based alternatives championed by hyperscalers such as AWS with Graviton, Google with Axion, Microsoft with Cobalt, and Nvidia with Grace and Vera. RISC-V is also emerging as a credible contender for specific workloads, particularly as open-source hardware ecosystems mature. Meanwhile, FPGAs continue to serve niche roles in low-latency and specialised inference applications.

Underpinning all of this is a complex and increasingly strained supply chain. Advanced semiconductor manufacturing is concentrated at TSMC, Samsung, and Intel Foundry, with leading-edge nodes below 5nm commanding the majority of AI chip demand. High Bandwidth Memory, supplied primarily by SK Hynix, Samsung, and Micron, has emerged as a critical bottleneck, while advanced packaging technologies such as CoWoS are operating at near-full capacity. Hyperscaler capital expenditure continues to flow into data centre construction, power infrastructure, and silicon procurement at a scale that is reshaping global semiconductor supply chains.

Geopolitics adds a further layer of complexity. US export controls on advanced AI chips have accelerated China's drive toward semiconductor self-sufficiency, with domestic players such as Huawei HiSilicon, Cambricon, Biren, and Hygon developing increasingly capable alternatives. The bifurcation of the global AI compute market into US-aligned and China-domestic supply chains is one of the defining structural trends of the decade, with profound implications for technology strategy, investment allocation, and national industrial policy.

The Global Market for Computing and AI for Data Centers 2026–2040 is a comprehensive strategic intelligence report covering the full landscape of data centre processor technology, market dynamics, competitive positioning, and long-range forecasting through to 2040. Produced for technology executives, semiconductor investors, strategic planners, and policy analysts, the report provides the depth of quantitative rigour and qualitative insight required to navigate one of the most rapidly evolving markets in the global economy.

The report opens with a set of preliminary materials including a detailed glossary of technical terms and abbreviations, a clear articulation of research objectives and scope, biographical profiles of the authoring team, and a candid retrospective on previous forecast accuracy. This is followed by a three-page summary and a full executive summary designed for senior readers who require rapid orientation to the report's key findings without sacrificing analytical depth.

Chapter one establishes the macroeconomic and geopolitical context, examining global AI infrastructure investment trends, hyperscaler capital expenditure trajectories for both US and Chinese players, the evolving regulatory landscape including US export controls, and the widening technology divide between Western and Chinese semiconductor ecosystems.

Chapter two forms the quantitative heart of the report, delivering granular market forecasts from 2021 to 2040 across all major processor categories. Revenue, average selling price, unit volume, wafer consumption, and server tray forecasts are provided at the vendor, product, and technology node level, enabling readers to build detailed bottom-up views of market opportunity and competitive exposure. Separate analytical lenses are provided for CPU, GPU, and AI ASIC dynamics, including HBM-driven revenue disaggregation and compute die forecasting.

Chapter three addresses the market forces shaping demand, including the falling cost of generative AI inference and training, the emergence of agentic and physical AI, the compute demands of recommendation engines and coding assistants, the competition between LLMs and traditional search, and broader questions around the CapEx and OpEx economics of AI infrastructure. An exploratory section examines the longer-term possibility of space-based data center architectures.

Chapter four maps the competitive landscape in detail, providing ecosystem maps for both the data center processor supply chain and the foundation model developer community. It includes financial benchmarking of leading chip designers, a deep-dive case study on OpenAI's revenue and compute trajectory, comprehensive market share analysis, and a dedicated section on Mainland China covering domestic market sizing, hyperscaler demand, manufacturer profiles, and supply chain structure.

Chapter five delivers an authoritative review of technology trends across all processor categories, covering process node roadmaps, chiplet architectures, rack-scale system designs, memory and packaging technology, and emerging computing paradigms including photonics, neuromorphic, and quantum computing. Unique assets include a full AI ASIC technology specification database and a start-up landscape analysis.

The report concludes with a forward-looking outlook chapter presenting bull, base, and bear case scenarios for the market through 2031 and beyond to 2040, a comprehensive risk register, and strategic recommendations. An extensive company profiles section — covering 81 organisations with one dedicated page per company — rounds out the report, providing standardised strategic and financial snapshots of every major player in the ecosystem.

Report Contents include: 

  • Global AI infrastructure and investment landscape
  • US and Chinese hyperscaler CapEx trends and projections
  • AI regulatory landscape and export controls
  • The US–China technology divide
  • Market Forecasts (2021–2040)
    • Total data centre processor revenue forecast
    • GPU, AI ASIC, CPU and FPGA revenue forecasts
    • Average selling price (ASP) forecasts by vendor and product tier
    • Processor unit shipment forecasts
    • Wafer starts by technology node and foundry (TSMC, Samsung, Intel Foundry)
    • GPU and AI ASIC compute die forecasts
    • HBM-driven revenue separation
    • Server tray volume forecasts
    • Dedicated CPU focus and GPU/AI ASIC focus sections
  • Market Trends
    • Cost of generative AI inference and training
    • From agentic AI to physical AI
    • Recommendation models for social networks
    • Coding assistants
    • Search engines vs. LLMs
    • OpenClaw
    • CapEx vs. OpEx in the generative AI era
    • The future of space-based AI data centres
  • Market Share & Supply Chain
    • Data center ecosystem map
    • Foundation models ecosystem map
    • US vs. China tech war timeline
    • Financial metrics of data center chip designers
    • Case study: OpenAI revenue and gigawatt forecast
    • Market share analysis — CPU, GPU, AI ASIC, XPU co-designers
    • Mainland China focus: market size, hyperscaler demand, manufacturer profiles, supply chain
  • Technology Trends
    • CPU: x86, Arm, RISC-V, workload specialisation
    • GPU: process nodes, chiplets, rack-scale architecture, HBM integration, interconnects
    • AI ASIC: hyperscaler roadmaps, start-up landscape, specification database, disaggregated inference
    • GPU vs. AI ASIC comparative analysis
    • Advanced packaging and HBM (HBM2E through HBM4), CoWoS, AI rack bill of materials
    • Emerging computing: photonics, neuromorphic, quantum
  • Outlook
    • Market outlook 2026–2040 with bull/base/bear scenarios
    • Technology outlook 2026–2040
    • Key risks and opportunities
    • Strategic recommendations
  • Company Profiles
    • 81 individual company profiles, one page per company, covering strategy, products, financials, and roadmap. Companies profiled include 01.AI, Achronix Semiconductor, Advanced Micro Devices (AMD), AI21 Labs, Alchip Technologies, Aleph Alpha, Alibaba Group / T-Head Semiconductor, Amazon Web Services (AWS), Ampere Computing, Anthropic, Arm Holdings, Axelera AI, Baidu, Biren Technology, Broadcom, ByteDance, Cambricon Technologies, Cerebras Systems, China Mobile, Cisco Systems, Cohere, CoreWeave, d-Matrix, DeepSeek, Dell Technologies, Enflame Technology, Esperanto Technologies, Etched, Fujitsu, Furiosa AI, GlobalFoundries (GF), Google (DeepMind / TPU Programme), GrAI Matter Labs, Graphcore, Groq, GUC (Global Unichip Corp.), Hewlett Packard Enterprise (HPE), HiSilicon Technologies, Huawei Technologies, Hygon Information Technology, IBM, Iluvatar CoreX, Intel Corporation, Kalray, Lattice Semiconductor, Lightmatter and more......

 

 

 

 

PRELIMINARY SECTIONS

  • Glossary of Terms and Abbreviations             i
  • Objective of the Report           iii
  • Scope of this Report  v
  • About the Authors       vii
  • What We Got Right, What We Got Wrong    ix
  • 3-Page Summary         xi
  • Executive Summary  xiv

 

CHAPTER 1 — CONTEXT

  • 1.1  Global AI Infrastructure and Investment Landscape  33
  • 1.2  US and Chinese Hyperscaler CapEx Trends and Projections                39
  • 1.3  AI Regulatory Landscape and Export Controls               45
  • 1.4  The US–China Technology Divide            51

 

CHAPTER 2 — MARKET FORECASTS

  • 2.1  Processor Revenue Forecast      57
    • 2.1.1  Total Data Center Processor Market, 2021–2040 ($B)          59
    • 2.1.2  GPU Revenue Forecast, 2021–2040 ($B)       64
    • 2.1.3  AI ASIC Revenue Forecast, 2021–2040 ($B) 68
    • 2.1.4  Server CPU Revenue Forecast, 2021–2040 ($B)       71
    • 2.1.5  FPGA Data Center Revenue Forecast, 2021–2040 ($M)       74
  • 2.2  Average Selling Price (ASP) Forecast     77
    • 2.2.1  GPU ASP Trends by Product Tier, 2021–2040 ($K)    78
    • 2.2.2  AI ASIC ASP Trends by Hyperscaler, 2021–2040 ($K)             81
    • 2.2.3  CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040  84
  • 2.3  Processor Volume Forecast        87
    • 2.3.1  GPU Unit Shipments by Vendor, 2021–2040 (K units)            88
    • 2.3.2  AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units)          92
    • 2.3.3  CPU Unit Shipments by Vendor, 2021–2040 (M units)           96
  • 2.4  Wafer Forecast    101
    • 2.4.1  GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040         102
    • 2.4.2  Wafer Starts by Foundry (TSMC, Samsung, Intel Foundry) 106
    • 2.4.3  GPU & AI ASIC Compute Die Forecast, 2021–2040 109
    • 2.4.4  HBM-Driven Revenue Separation from GPU & AI ASIC          112
  • 2.5  Server Tray Volume Forecast      115
  • 2.6  CPU Focus             121
  • 2.7  GPU & AI ASIC Focus       127

 

CHAPTER 3 — MARKET TRENDS

  • 3.1  Cost of Generative AI Inference and Training  137
  • 3.2  From Agentic AI to Physical AI   148
  • 3.3  Recommendation Models for Social Networks              158
  • 3.4  Coding Assistants            164
  • 3.5  Search Engine vs. LLM    170
  • 3.6  OpenClaw              176
  • 3.7  CapEx vs. OpEx in the Era of Generative AI        181
  • 3.8  Is the Future of AI Data Centers in Space?        193

 

CHAPTER 4 — MARKET SHARE & SUPPLY CHAIN

  • 4.1  Data Center Ecosystem Map     203
  • 4.2  Foundation Models Ecosystem Map     212
  • 4.3  U.S. vs. China Tech War — Timeline      219
  • 4.4  Financial Metrics of Data Center Chip Designers         227
  • 4.5  Case Study: OpenAI Revenue and Gigawatt     238
  • 4.6  Market Share: CPU, GPU, AI ASIC & XPU Co-Designers            246
    • 4.6.1  GPU Market Share by Revenue and Units      247
    • 4.6.2  AI ASIC Market Share by Hyperscaler               251
    • 4.6.3  CPU Market Share by Vendor 255
    • 4.6.4  XPU Co-Designer Revenue Market Share      259
  • 4.7  Focus on Mainland China            263
    • 4.7.1  Chinese DC Processor Market Size & Forecast          264
    • 4.7.2  Chinese Hyperscaler Processor Demand     267
    • 4.7.3  Chinese Processor Manufacturer Profiles & Roadmaps     271
    • 4.7.4  China DC Processor Supply Chain    274

 

CHAPTER 5 — TECHNOLOGY TRENDS

  • 5.1  CPU Technology Trends 279
    • 5.1.1  x86 Architecture Evolution      281
    • 5.1.2  Arm-Based CPU Momentum in the Data Center       285
    • 5.1.3  RISC-V in the Data Center        289
    • 5.1.4  CPU Specialisation for AI Workloads               293
  • 5.2  GPU Technology Trends 299
    • 5.2.1  Process Node Roadmap and Transition         300
    • 5.2.2  Chiplet and Multi-Die Architectures  304
    • 5.2.3  Rack-Scale GPU Architectures (NVL72 and Beyond)             308
    • 5.2.4  Memory Bandwidth and HBM Integration      313
    • 5.2.5  Networking and Interconnect Evolution         317
  • 5.3  AI ASIC Technology Trends          323
    • 5.3.1  Hyperscaler ASIC Product Roadmaps            324
    • 5.3.2  AI ASIC Start-Up Landscape  330
    • 5.3.3  AI ASIC Technology Specification Database                335
    • 5.3.4  Compute Disaggregation for AI Inference      341
  • 5.4  GPU vs. AI ASIC: Comparative Analysis              347
  • 5.5  Advanced Packaging and HBM Memory             355
    • 5.5.1  HBM Technology Roadmap (HBM2E to HBM4)          356
    • 5.5.2  CoWoS and Advanced Packaging Capacity 360
    • 5.5.3  Custom HBM and Co-Design Trends                363
    • 5.5.4  AI Rack Bill of Materials             366
  • 5.6  Emerging Computing Architectures       371
    • 5.6.1  Photonic Computing   372
    • 5.6.2  Neuromorphic Computing      374
    • 5.6.3  Quantum Computing Outlook              376

 

CHAPTER 6 — OUTLOOK

  • 6.1  Market Outlook 2026–2040        381
  • 6.2  Technology Outlook 2026–2040              386
  • 6.3  Key Risks and Opportunities      390
  • 6.4  Strategic Recommendations     394

 

CHAPTER 7 — COMPANY PROFILES 399-480 (81 company profiles)

 

List of Figures 

  • Fig. 1.1               Global AI Infrastructure Investment Forecast, 2021–2040 ($B)    34
  • Fig. 1.2               US vs. Chinese Hyperscaler CapEx, 2021–2040 ($B)          39
  • Fig. 1.3               Data Center Power Consumption Forecast, 2024–2040 (GW)      40
  • Fig. 1.4               AI-Related Data Center Construction Starts by Region, 2022–2028         42
  • Fig. 1.5               US Export Controls on AI Chips — Key Milestones, 2019–2026   46
  • Fig. 1.6               US–China Technology Decoupling Timeline, 2018–2026  51
  • Fig. 2.1               Total Data Center Processor Market Revenue Forecast, 2021–2040 ($B)              60
  • Fig. 2.2               Revenue Breakdown by Processor Type (CPU, GPU, AI ASIC, FPGA), 2021–2040             61
  • Fig. 2.3               Data Center Processor CAGR by Category, 2025–2040 (%)            62
  • Fig. 2.4               GPU Market Revenue Forecast, 2021–2040 ($B)    64
  • Fig. 2.5               GPU Revenue Split by Vendor (Nvidia, AMD, Others), 2021–2040              65
  • Fig. 2.6               Nvidia GPU Revenue by Product Generation, 2021–2028 ($B)      66
  • Fig. 2.7               AMD GPU Revenue by Product Generation, 2021–2028 ($B)         67
  • Fig. 2.8               AI ASIC Market Revenue Forecast, 2021–2040 ($B)             68
  • Fig. 2.9               AI ASIC Revenue Split by Hyperscaler, 2021–2040 69
  • Fig. 2.10            Server CPU Market Revenue Forecast, 2021–2040 ($B)    71
  • Fig. 2.11            Server CPU Revenue Split by Architecture (x86 vs. Arm), 2021–2040       72
  • Fig. 2.12            FPGA Data Center Revenue Forecast, 2021–2040 ($M)    74
  • Fig. 2.13            GPU ASP Evolution by Product Tier, 2021–2040 ($K)           78
  • Fig. 2.14            AI ASIC ASP Trends by Hyperscaler, 2021–2040 ($K)           81
  • Fig. 2.15            Server CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 ($)         84
  • Fig. 2.16            GPU Unit Shipments by Vendor, 2021–2040 (K units)          88
  • Fig. 2.17            Nvidia GPU Unit Shipments by Product Generation, 2021–2028 89
  • Fig. 2.18            AMD GPU Unit Shipments by Product Generation, 2021–2028    90
  • Fig. 2.19            AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units)        92
  • Fig. 2.20            Google TPU Unit Deployment Forecast, 2021–2040            93
  • Fig. 2.21            AWS Trainium & Inferentia Unit Forecast, 2021–2040        94
  • Fig. 2.22            Microsoft MAIA Unit Forecast, 2021–2040 95
  • Fig. 2.23            CPU Unit Shipments — Data Center, 2021–2040 (M units)             96
  • Fig. 2.24            Intel vs. AMD CPU Market Share in Unit Terms, 2021–2040 (%)    97
  • Fig. 2.25            Hyperscaler Custom CPU Unit Adoption, 2022–2040 (M units)   98
  • Fig. 2.26            GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040 (KW/month)          102
  • Fig. 2.27            Wafer Consumption Split: Advanced Nodes (<5nm, 5nm, 7nm), 2021–2040     103
  • Fig. 2.28            GPU & AI ASIC Wafer Starts by Foundry, 2021–2040           106
  • Fig. 2.29            TSMC Advanced Node Capacity Forecast, 2024–2040 (KW/month)         107
  • Fig. 2.30            GPU & AI ASIC Compute Die Forecast, 2021–2040              109
  • Fig. 2.31            Average Die Size Trend — GPU vs. AI ASIC, 2021–2040 (mm²)       110
  • Fig. 2.32            HBM Revenue Separated from GPU & AI ASIC Total, 2021–2040 ($B)      112
  • Fig. 2.33            AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units)              115
  • Fig. 2.34            AI Server Rack Configuration and Architecture, 2025–2040           117
  • Fig. 2.35            CPU Market Share by Revenue — Intel vs. AMD vs. Arm-based, 2021–2040       121
  • Fig. 2.36            Hyperscaler Arm CPU Deployment Ramp, 2022–2040     122
  • Fig. 2.37            CPU Product Roadmap — Intel, AMD, Arm, Google, AWS, Nvidia, 2024–2030  124
  • Fig. 2.38            GPU Market Share by Revenue, 2021–2040 (%)      127
  • Fig. 2.39            AI ASIC Market Share by Deployment Volume, 2021–2040 (%)     129
  • Fig. 2.40            GPU & AI ASIC Split by Technology Node, 2021–2040        131
  • Fig. 3.1               Cost per Token Trend — Training and Inference, 2021–2040 ($/M tokens)             139
  • Fig. 3.2               Training Compute Requirements by Model Type, 2020–2028 (FLOPs)     141
  • Fig. 3.3               Inference Cost Breakdown by Infrastructure Component, 2025 (%)         143
  • Fig. 3.4               Token Cost Reduction Roadmap, 2025–2040 ($/M tokens)             145
  • Fig. 3.5               AI Model Parameter Count vs. Hardware Requirements, 2020–2028       147
  • Fig. 3.6               Agentic AI Market Taxonomy and Use Cases            152
  • Fig. 3.7               AI Agent Deployment Forecast by Sector, 2025–2040        154
  • Fig. 3.8               Physical AI Hardware Requirements vs. Generative AI, 2025–2040          156
  • Fig. 3.9               Robotics Semiconductor Market Forecast, 2024–2040 ($B)          158
  • Fig. 3.10            Recommendation Model Architecture Evolution, 2018–2028      164
  • Fig. 3.11            Recommendation Model Compute Demand by Platform, 2024–2040    166
  • Fig. 3.12            AI-Powered Coding Assistant Market Share, 2024–2028 (%)         171
  • Fig. 3.13            Coding AI GPU Compute Demand, 2024–2040      173
  • Fig. 3.14            LLM vs. Traditional Search: Query Volume Forecast, 2022–2040 178
  • Fig. 3.15            AI Search Compute Infrastructure Requirements, 2024–2040     180
  • Fig. 3.16            CapEx Cycle — US Hyperscalers, 2015–2040 ($B)               182
  • Fig. 3.17            CapEx-to-Revenue Ratio — Major Hyperscalers, 2020–2040 (%)               184
  • Fig. 3.18            AI Infrastructure OpEx vs. CapEx Split, 2024–2040              186
  • Fig. 3.19            Cloud AI Chip Rental vs. Ownership Economics, 2025–2040       188
  • Fig. 3.20            Space-Based Data Center Conceptual Architecture           201
  • Fig. 3.21            Low Earth Orbit Latency and Bandwidth Projections, 2025–2035              203
  • Fig. 4.1               Global Data Center Processor Ecosystem Map      203
  • Fig. 4.2               AI Chip Supply Chain — From Silicon to Hyperscaler          205
  • Fig. 4.3               Co-Designer and Hyperscaler Relationship Map   208
  • Fig. 4.4               OSAT and Advanced Packaging Supply Chain Map              210
  • Fig. 4.5               Foundation Models Ecosystem Map — Developers and Infrastructure   212
  • Fig. 4.6               Open vs. Closed Source AI Model Landscape, 2024           215
  • Fig. 4.7               Foundation Model Training Infrastructure by Developer    217
  • Fig. 4.8               US Export Control Timeline — Semiconductors, 2018–2026         219
  • Fig. 4.9               Chinese AI Chip Import Replacement Progress, 2022–2028 (%) 222
  • Fig. 4.10            Sanctioned vs. Unsanctioned Chinese AI Chip Revenues, 2022–2028   225
  • Fig. 4.11            Comparative Revenue — Data Center Chip Designers, 2021–2025 ($B) 227
  • Fig. 4.12            Gross Margin Comparison — Nvidia vs. AMD vs. Intel, 2020–2025 (%)   229
  • Fig. 4.13            R&D Spend as % of Revenue — Key Chip Designers, 2020–2025 231
  • Fig. 4.14            AI Semiconductor Start-Up Fundraising, 2019–Q1 2026 ($M)      234
  • Fig. 4.15            OpenAI Revenue Forecast, 2023–2030 ($B)              238
  • Fig. 4.16            OpenAI Compute Demand (Gigawatt), 2023–2030              240
  • Fig. 4.17            OpenAI GPU Procurement Forecast by Generation, 2023–2028 242
  • Fig. 4.18            GPU Market Share by Revenue, 2021–2025 (%)      247
  • Fig. 4.19            GPU Market Share by Units, 2021–2025 (%)              248
  • Fig. 4.20            Nvidia, AMD, Google, AWS GPU/ASIC Unit Split, 2021–2028         250
  • Fig. 4.21            AI ASIC Market Share by Hyperscaler, 2021–2025 (%)        251
  • Fig. 4.22            XPU Co-Designer Revenue — Broadcom, Marvell, MediaTek, Alchip, GUC, 2023–2026                252
  • Fig. 4.23            CPU Market Share by Revenue — Intel vs. AMD vs. Arm, 2021–2025 (%)               255
  • Fig. 4.24            Hyperscaler Custom CPU Market Share Evolution, 2022–2028   257
  • Fig. 4.25            XPU Co-Designer Revenue Share — Broadcom, Marvell, Others, 2021–2026    259
  • Fig. 4.26            Chinese DC Processor Market Size, 2021–2028 ($B)          264
  • Fig. 4.27            Chinese Hyperscaler Processor Demand Forecast, 2021–2028 267
  • Fig. 4.28            Chinese Processor Maker Market Share (Unit), 2024 & 2025         269
  • Fig. 4.29            HiSilicon, Cambricon, Baidu, Hygon DC Processor Roadmap     271
  • Fig. 4.30            China DC Processor Supply Chain Map       274
  • Fig. 5.1               CPU Architecture Comparison — x86, Arm, RISC-V for the Data Center 282
  • Fig. 5.2               Arm Server CPU Shipment Forecast, 2022–2040 (M units)             286
  • Fig. 5.3               RISC-V Data Center Adoption Forecast, 2025–2040           290
  • Fig. 5.4               CPU Specialisation for AI Inference Workloads      293
  • Fig. 5.5               GPU Process Node Roadmap — Nvidia, AMD, 2020–2030             300
  • Fig. 5.6               GPU Die Size Evolution and Chiplet Transition, 2020–2030 (mm²)             304
  • Fig. 5.7               Rack-Scale GPU Architecture — NVL72 and Next-Generation Platforms              308
  • Fig. 5.8               GPU Memory Bandwidth Trend — HBM Generations, 2020–2030 (TB/s) 313
  • Fig. 5.9               NVLink and Interconnect Bandwidth Evolution, 2020–2030          317
  • Fig. 5.10            Hyperscaler ASIC Roadmap Comparison — Google, AWS, Microsoft, Meta       324
  • Fig. 5.11            AI ASIC Start-Up Landscape by Funding Stage, 2024          330
  • Fig. 5.12            AI ASIC Technology Specification Matrix (Selected Companies) 335
  • Fig. 5.13            Disaggregated Inference Architecture Diagram      341
  • Fig. 5.14            GPU vs. AI ASIC: Performance per Watt Comparison, 2022–2026             347
  • Fig. 5.15            GPU vs. AI ASIC: Training vs. Inference Suitability Matrix  349
  • Fig. 5.16            GPU vs. AI ASIC: Total Cost of Ownership Analysis              351
  • Fig. 5.17            HBM Technology Roadmap — HBM2E to HBM4, 2020–2028         356
  • Fig. 5.18            HBM Bandwidth and Capacity per Stack by Generation, 2020–2028       357
  • Fig. 5.19            CoWoS Capacity Expansion Roadmap — TSMC, 2022–2028        360
  • Fig. 5.20            Advanced Packaging Market Share — CoWoS, SoIC, Others, 2024–2028             362
  • Fig. 5.21            Custom HBM Co-Design Relationships Map            363
  • Fig. 5.22            AI Server Rack Bill of Materials — Component Breakdown, 2025 ($K)     366
  • Fig. 5.23            AI Rack BoM Cost Evolution, 2023–2028 ($K)          368
  • Fig. 5.24            Silicon Photonics Market Forecast in Data Centers, 2024–2040 ($B)      372
  • Fig. 5.25            Neuromorphic Computing Roadmap, 2024–2040 374
  • Fig. 5.26            Quantum Computing Timeline to Commercial Viability, 2025–2040        376
  • Fig. 6.1               Data Center Processor Market Scenario Analysis, 2026–2040 ($B)          382
  • Fig. 6.2               Bull, Base, Bear Case Revenue Scenarios by Processor Type, 2040         384
  • Fig. 6.3               Technology Roadmap Summary — CPU, GPU, AI ASIC, 2026–2040         386
  • Fig. 6.4               Competitive Landscape Risk Matrix, 2026–2040  390
  • Fig. 6.5               Investment Opportunity Map — Data Center Semiconductor Ecosystem            393

 

List of Tables  

  • Table 2.1           Data Center Processor Market Revenue Summary, 2021–2040 ($B)        60
  • Table 2.2           GPU Revenue by Vendor, 2021–2040 ($B)  65
  • Table 2.3           AI ASIC Revenue by Hyperscaler, 2021–2040 ($B) 69
  • Table 2.4           Server CPU Revenue by Vendor, 2021–2040 ($B)   72
  • Table 2.5           GPU ASP by Product Tier, 2021–2040 ($K)  79
  • Table 2.6           AI ASIC ASP by Hyperscaler, 2021–2040 ($K)           82
  • Table 2.7           GPU Unit Shipments by Vendor, 2021–2040 (K units)          89
  • Table 2.8           AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units)        93
  • Table 2.9           CPU Unit Shipments by Vendor, 2021–2040 (M units)        97
  • Table 2.10        GPU & AI ASIC Wafer Starts by Node and Foundry, 2021–2040    103
  • Table 2.11        AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units)              116
  • Table 2.12        CPU Processor Roadmap Summary — Major Vendors, 2024–2030          125
  • Table 2.13        GPU & AI ASIC Product Roadmap Summary, 2024–2030 133
  • Table 3.1           Cost per Token by Model Size and Hardware Configuration, 2024–2040 140
  • Table 3.2           Agentic AI Use Cases by Industry and Hardware Requirements  153
  • Table 3.3           Coding Assistant Market Share and Underlying Infrastructure, 2024       172
  • Table 4.1           Financial Metrics — Top 10 Data Center Chip Designers, 2021–2025     228
  • Table 4.2           US and Chinese Hyperscaler CapEx Summary, 2021–2026 ($B) 233
  • Table 4.3           AI Semiconductor Start-Up Fundraising Database, 2019–Q1 2026           235
  • Table 4.4           GPU Market Share Summary by Revenue and Units, 2021–2025 248
  • Table 4.5           AI ASIC Specifications — Google, AWS, Microsoft, Meta, 2024–2026     253
  • Table 4.6           Chinese Data Center Processor Manufacturer Overview 272
  • Table 4.7           China DC Processor Supply Chain — Key Component Suppliers                274
  • Table 5.1           CPU Specifications — Intel, AMD, AWS, Google, Microsoft, Huawei, Nvidia, 2024–2026                284
  • Table 5.2           GPU Specifications — Nvidia Blackwell, Rubin; AMD MI350X, MI450, 2024–2026         302
  • Table 5.3           AI ASIC Technology Specification Database (Full, All Major Vendors)      336
  • Table 5.4           HBM Specification Comparison — HBM2E, HBM3, HBM3E, HBM4           357
  • Table 5.5           AI Server Rack BoM — Itemised Cost Breakdown, 2025 ($K)         367
  • Table 5.6           Emerging Computing Technology Readiness Assessment              377
  • Table 6.1           Market Forecast Summary — Bull / Base / Bear Scenarios, 2026–2040 ($B)       383
  • Table 6.2           Key Risk Register — Probability and Impact Assessment 391

 

 

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  • Mid-year Update

 

The Global Market for Computing and AI for Data Centers 2026–2040
The Global Market for Computing and AI for Data Centers 2026–2040
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Price: £1,100.00

The Global Market for Computing and AI for Data Centers 2026–2040
The Global Market for Computing and AI for Data Centers 2026–2040
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Price: £1,200.00

 

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