Computing and AI for Data Centers 2026

SCOPE

• Depth analysis
  • System
  • Device
  • Wafer
• Technology 
  • Data center processors:
  • CPU
  • GPU
  • AI ASIC
  • FPGA

MARKET

• Device units
• Value
• Wafer (device)

PLAYERS

• Market shares
• Ecosystem / Supply chain
• Strategy / Financial analysis
• Business news

TECHNOLOGY

• Technology status
• Technology Roadmap

What's new

• Nvidia, AMD, Google and AWS 2021-2028 unit split by processors
• 2021-2028 processor forecast for Intel, Huawei, Microsoft, Meta, OpenAI, Baidu, Cambricon
• 2024 & 2025 processor maker Market share in unit for the Chinese market
• GPU & AI ASIC 2021-2031 split by techno node
• GPU & AI ASIC compute die forecast 2021-2031
• AI agent trend description and impact on processors
• Open AI 2023-2030 compute GW and revenue forecast
• 2023-2026 Broadcom, Marvell, MediaTek, Alchip, GUC Data Center XPU revenue
• Co-designers & hyperscalers relationship evolution
• Co-designer & custom HBM
• HiSilicon, Cambricon, Baidu, Hygon DC processor roadmap
• China DC processor supply chain
• US and Chinese hyperscalers CapEx
• Compute disaggregation for AI Inference description
• CPU specialization for workload description
• Arm and RISC V CPU program description
• 2021-2031 GPU and AI ASIC compute die number evolution
• HBM-driven revenue separation from total GPU and AI ASIC revenue
• AI ASIC technology specification database
• AI ASIC start-up database
• AI rack Bill of materials
• Startups fundraising 2019 - Q1 2026

Report's objectives 

• Comprehensive analysis of the data center processor industry:
  • Examine the data center processor market (CPU, GPU, AI ASIC, FPGA), including revenue and unit forecasts, ASP evolution, wafer starts (by node and foundry).
  • Focus on AI and Generative AI, while also covering the non-AI data center market.
  • Provide a detailed product breakdown for the main GPU, AI ASIC, and CPU players:
    • Nvidia: H100, H200, H20 and H200 for China, B100, B200, B300, Rubin, Rubin CPX, Rubin Ultra, Feynman, Feynman Ultra.
    • AMD: MI100, MI200, MI300X, MI308 for China, MI325X, MI300A, MI350X, MI375X, MI450, MI500.
    • Google: TPU v4/4i, v5e, v5p, v6e Trillium, v7x Ironwood, v7p, v8x, v8ax.
    • AWS: Inferentia 1 & 2, Trainium 1, 2, 3, 4, 5.
    • Other AI ASICs: Intel Gaudi, Huawei Ascend, Microsoft MAIA, META MTIA, Tesla Dojo, OpenAI Titan, Baidu Kunlun, and Cambricon.
    • CPUs: Intel Xeon, AMD EPYC, Google Axion, AWS Graviton, Microsoft Cobalt, Huawei Kunpeng, Nvidia Grace/Vera, Ampere Computing, Arm AGI CPU, Alibaba Yitian, IBM.
• Global understanding of the ecosystem and key players:
  • Identify major competitors at each level of the processor and AI software supply chain.
  • Examine the relationships and interdependencies within the ecosystem.
  • Determine the key beneficiaries of the generative AI momentum.
  • Anticipate changes and shifts in the industry for the coming years.
• Key technical insights and future technology trends:
  • Discuss key processor technology choices and the dynamics shaping the industry.
  • Review the main data center processor players' roadmaps and technology development plans.
  • Compare and contrast GPU and AI ASIC performance, highlighting strengths and weaknesses.
  • Identify potential challenges and opportunities in the development and adoption of generative AI technologies in the semiconductor industry.

Supported by strong AI-related demand, the data center processor market is forecast to reach $488B by 2027

The data center processor market is growing rapidly, driven by rising demand for generative AI workloads, and is expected to expand from $215B in 2025 to $656B by 2031. GPU remain the largest segment, with a CAGR of 20% through 2031, supported by both training and inference demand. AI ASIC is the fastest-growing category, with a projected CAGR of 35% through 2031 as hyperscalers scale internal deployments. Meanwhile, the server CPU market continues to post solid growth, driven by the rise of agentic AI workloads, while the FPGA segment remains relatively small and broadly stable over the forecast period.

$685B in U.S. hyperscalers’ CapEx for 2026 sustains strong momentum across the AI semiconductor ecosystem

Generative AI is accelerating the shift toward more specialized data center processors while increasing pressure on the broader semiconductor supply chain. Intel and AMD still lead the server CPU market, but x86 faces growing competition from hyperscalers developing their own Arm-based processors, while Nvidia is also expanding its position with Grace & Vera. In GPUs, Nvidia remains overwhelmingly dominant, whereas AI ASICs are gaining traction through a more diversified set of players, although the market remains much smaller than GPUs because many chips are designed for internal use.

At the same time, hyperscalers are relying more heavily on external chip design partners, reflecting the growing complexity of AI processor development, especially in areas such as HBM sourcing, chiplets, photonics, and advanced packaging. This rapid expansion is being supported by record hyperscaler CapEx, but it is also creating major risks around power availability and supply of key components such as HBM, CoWoS, and advanced-node wafers.

From advanced nodes and chiplets to rack-scale architecture and inference disaggregation

Generative AI is reshaping data center computing and driving innovation across all processor categories, with the main objective being better performance and lower cost per token. In CPUs, AMD and Intel still dominate, but Arm-based architectures are gaining ground, especially among hyperscalers, thanks to their stronger energy efficiency and scalability. GPUs and AI ASICs are evolving along similar lines, with advanced process nodes, larger chiplet-based designs, rack-scale architectures, and much higher HBM capacity to support increasingly demanding AI workloads. At the same time, new approaches such as disaggregated inference and HBM-free architectures based on large on-die SRAM are attracting growing interest as the industry looks for more efficient ways to run LLMs. Meanwhile, FPGAs continue to play an important role in low-latency and specialized workloads, supported by more heterogeneous and chiplet-based designs.

Glossary

Objective of the report

Scope of this report

About the authors

What we got right, what we got wrong

3-page summary

Executive summary

Context

Market forecasts

• Processor revenue forecast
• ASP forecast
• Processor volume forecast
• Wafer forecast
• Server tray volume forecast
• CPU focus
• GPU & AI ASIC

Market trends

• Cost of Generative AI inference and training
• From Agentic AI to Physical AI
• Recommendation models for social networks
• Coding assistants
• Search engine vs LLM
• OpenClaw
• CapEx vs OpEx in the era of Generative AI
• Is the future of AI data centers in space?

Market share & supply chain

• Data center ecosystem map
• Foundation models ecosystem map
• U.S. Vs. China tech war – Timeline
• Financial metrics of data center chip designers
• Case study: OpenAI revenue and gigawatt
• Market Share CPU, GPU, AI ASIC, XPU co-designers
• Focus on Mainland China 

Technology trends

• CPU
• GPU
• AI ASIC
• GPU vs AI ASIC
• Advanced Packaging and HBM memory
• Emerging computing

Outlook

Related reports and teardown

Achronix, Adobe, Advanced Micro Devices (AMD), AI21labs, Alchip, Aleph Alpha, Alibaba Group, Ambient Scientific, Amlogic, Ampere Computing, Analog inference, Anthropic, Apple, Anaflah, AONdevices, Arm, Asus, Atos, Amazon Web Services (AWS), Axelera AI, Axera, Azure Engine, Baidu, Biren Technology, Bitmain, BlueOceanSmart, Bluetrum, Blumind, Broadcom, ByteDance, Cambricon Technologies, Canaan, Cerebras, Ceremorphic, China Mobile, China Telecom, Cisco, Clouder, Cognifiber, Cohere, Compal, Corigine, Core scientific, CoreWeave, Corerain Technologies, Cornami, Cray, Dayu, DeepL DeepSeek, Deepx, Deezer, Dell, Digital Reality, Dell, d-Matrix, Denglin, Electronic Arts, Enflame, Equinix, Epic Semiconductors, Esperanto Technologies, etched, Eviden, Fulhan, Fujitsu, Fungible, Furiosa, Glenfly, Global Foxcom, GlobalFoundries (GF), GigaByte, GitHub, Google, Gowin, GrAI Matter Labs, Graphcore, Groq, GUC, Guoxin Micro, Gyrfalcon technology, Habana, HiSilicon, Hitachi, Hewlett, Houmo AI, Packard Enterprise (HPE), HjMicro, Huawei, Hugging Face, Hygon, IBM, Iluvatar CoreX, Icubecorp, Inflection AI, Innosilicon, Intel, Inventec, InspireSemi, Inspur, Jaguar, Jingjia Micro, Kalray, KnuEdge, Krutrim, Lattice Semiconductor, Lenovo, Lightelligence, LG, Lightmatter, LiSuan Tech, Loongson Technology, Luminous Computing, Lynxi, Marvell technology, MediaTek, Mellanox, Meta, Metax-tech, Microchip, Micron, Microsoft Azure, Mistral AI, Moffett AI,  Mobilint, Moonshot AI, Moore Threads, Mythic, NebulaMatrix, NEC, Neuchip,  NetApp, Netflix, Neuroblade, Neureality, Netronome, NTT Communications, Nuovoton, Nuvia, Nvidia, NXP, OpenAI, Oracle, Optalysys, Penguin Computing, Pensando, Perceive, Perplexity, Pezy Computing, Phytium, PyTorch, Qilingxin, Quanta Cloud Technology (QCT), Qualcomm, Quality Technology Services (QTS), Quanta Computer, Quillion, Rackspace, Rapidus, Rebellions, Recogni, Renesas, Resnics, Rivai, Rockchip, SambaNova, Samsung, Sapeon, Sabey Data Centers, SAP, Semiconductor Manufacturing International Corporation (SMIC), Semron, SenseTime, Shingroup, SiPearl, SK hynix, Socionext, Softbank, Sony, St Microelectronics, Starfivetech, SuperMicro, Synaptics, Syntiant, Tachyum, T-Head, Taiwan Semiconductor Manufacturing Company (TSMC), Tencent, Tenstorrent, Tesla, Texas Instruments, Tower Semiconductor, Toshiba, Twitter, United Microelectronics Corporation (UMC), UniSOC, Untether AI, Vastaitech, Vertical compute, X, X.ai, Xilinx, Xmos, Wave Computing, Weibo, Ysemi, Yunsilicon, Yusur, Zhipu.AI, ZTE, 01.AI, and more