Global AI Server Compute ASIC Shipment by Chip Model, Hyperscalar, Design Partner & Foundry Forecast, Jan 2026
Overview: This workbook provides granular unit shipment forecasts and supply chain market share data from 2024 through 2027, supporting our latest research note and PR: "AI Server Compute ASIC Shipments to Triple by 2027 as Custom Silicon Enters Hyper-Growth Phase."
Key Insights & Datasets:
Hyperscaler Shipment Forecasts: Detailed unit breakdowns for top CSPs (including Google, AWS, Microsoft, and Meta) as the market accelerates toward vertical integration.
Vendor Market Share Analysis: Evolution of the design partner landscape, tracking the competitive dynamics between incumbents (Broadcom, Marvell) and challengers (MediaTek, Alchip).
Supply Chain Trend: Analysis of the structural pivot from training-focused silicon to cost-optimized inference solutions and the emerging impact of dual-sourcing strategies.
The workbook includes a detailed AI Server Compute ASIC Shipment Outlook, fully segmented by the following dimensions:
- CSP: (Google, Microsoft, AWS, Meta, xAI, OpenAI, ByteDance, etc.)
- ASIC Model: (TPU, Trainium, Maia, MTIA, etc.)
- Design Partner: (Broadcom, Marvell, Alchip, MediaTek, GUC, etc.)
- Foundry: (e.g., TSMC)
- Process Node: (5nm, 3nm, 2nm, etc.)
- Packaging Type: (CoWoS, EMIB, SoIC, etc.)
- Memory Configuration: (HBM Type)
Table of content:
- Assumptions and Analysis
- Definitions
- Summary
- Pivot Table
- Flatfile
Published Date: 3 February 2025
Category
Industry
Semiconductors
Service
HPC, Cloud AI
Report Type
Report
Time Period
Other
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Author
Brady Wang
Hi, I’m Brady Wang, a seasoned professional with over 20 years of experience in the high-tech industry, spanning semiconductor manufacturing, market intelligence, and strategic advisory roles. Currently, I serve as an analyst at Counterpoint Research, where I specialize in semiconductors with a focus on advanced applications such as automotive, server platforms, and cutting-edge process nodes. My core research centers on AI servers and their key components, including GPUs, custom accelerators, high-bandwidth memory (HBM), CPUs, and advanced packaging technologies. I also track the evolution of AI server architectures, interconnect technologies, and data center deployment trends. By combining deep technical knowledge with market insight, I help clients navigate the fast-changing AI infrastructure landscape and make strategic, data-driven decisions.
David Wu
David Wu is a Research Associate at Counterpoint Research in Taiwan, dedicated to foundry and semiconductor research, providing insights into market trends and key industry developments. Prior to joining Counterpoint Research, he gained experience in Deal Advisory, Private Equity, and TMT Corporate Banking, where he built a solid foundation in industry research and financial analysis. David Wu holds a double bachelor’s degree in Accounting and International Business from National Chengchi University.