이기종 컴퓨팅 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the heterogeneous computing market size is expected to increase from USD 132.19 billion in 2025 to USD 160.17 billion in 2026 and reach USD 430.52 billion by 2031, growing at a CAGR of 21.87% over 2026-2031.

This report is Segmented by Component (Hardware, Software, and Services), Deployment Mode (On-Premises, Cloud, and Hybrid), Processor Type (CPU, GPU, FPGA, ASIC, and More), Application (Artificial Intelligence and Machine Learning, Data Center and Cloud Computing, and More), End User (Government and Public Sector, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Heterogeneous Computing Market Trends and Insights

Rising Generative AI and Large Language Model Workloads

Generative AI workloads are the strongest near-term force driving the heterogeneous computing market, pushing buyers to combine dense training accelerators with hardware optimized for high-volume inference. Large language models do not create demand solely through model training; they also impose a lasting serving burden that increases token throughput, memory access, and latency requirements long after deployment begins. That is making single-processor strategies less practical, since one architecture rarely delivers the best mix of performance, utilization, and power efficiency across the full AI workflow. Google's TPU 8t and TPU 8i launch in April 2026 clearly showed this split, with one design tuned for large-scale training and the other for concurrent inference and lower network latency. The heterogeneous computing market is therefore being shaped by platforms that can integrate different accelerator profiles rather than by raw chip speed alone. Vendors that can align silicon, interconnect, memory, and software around those needs are moving closer to repeat enterprise adoption.

Growing Need for Parallel Processing in AI Training and Inference

The growing need for parallel processing is widening the role of the heterogeneous computing market beyond hyperscale AI and into simulation, research, finance, and industrial computing. These workloads increasingly depend on coordinated processing across CPUs, GPUs, and specialized accelerators because no single device class handles every stage efficiently. Amazon's Graviton5 design, introduced in 2026 with a 4-chiplet structure, 420 GB/s inter-chiplet bandwidth, and stronger machine learning inference performance, showed that even CPU-class products are now being redesigned around parallel AI behavior. The same pattern is visible in software, where AMD's first 3-GPU heterogeneous submission in MLPerf Inference 6.0 highlighted how orchestration across different processor resources can become a direct performance lever. Academic work also supports this direction, with research published in Scientific Reports showing that learning-based scheduling on hybrid cloud-edge systems improves service quality by dynamically matching workloads to available hardware. As a result, the heterogeneous computing market is moving toward system-level optimization, where the value lies in how processors work together rather than in isolated chip specifications.

High Capital Intensity for Heterogeneous System Design and Validation

High capital intensity remains a major brake on the heterogeneous computing market because multi-processor systems require far more validation work than single-architecture deployments. Cost pressure appears at every stage, including silicon design, board development, interconnect tuning, memory integration, software compatibility testing, and system qualification. That burden is hardest on mid-sized enterprises and emerging-market operators that cannot spread engineering costs across very large, stable compute volumes. The result is a market where adoption can cluster among hyperscalers, large enterprises, and public institutions that have clearer workload visibility and stronger balance sheets. Advanced packaging requirements add another layer of cost and execution risk, narrowing the number of suppliers able to support production-scale deployment.

Other drivers and restraints analyzed in the detailed report include:

1. Expansion of Sovereign AI and National Compute Infrastructure
2. Shift Toward Energy-Efficient and Performance-Per-Watt Architectures
3. Scarcity of Parallel Programming and Hardware-Software Co-Design Talent

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Hardware accounted for 58.41% of the heterogeneous computing market in 2025, underscoring how strongly current spending still leans toward accelerator purchases, servers, memory, and supporting infrastructure. The largest near-term revenue pool remained tied to GPU-dense data center expansion, where buyers continued to prioritize access to computing capacity before focusing on full software standardization. That spending pattern also reflected timing, since large hardware rollouts reached commercial scale faster than orchestration and scheduling software could be monetized across broad enterprise environments. The heterogeneous computing market, therefore, entered 2026 with hardware still carrying the largest share of recognized value, even though long-term differentiation is moving higher in the stack.

GPU, FPGA, ASIC, and CPU products all expanded their installed base, but GPUs drove the largest absolute revenue contribution because they remained central to both training and many inference tasks. AMD's Instinct MI430X, presented for scientific and AI workloads with 432 GB of HBM4 memory and 19.6 TB/s bandwidth, illustrated how hardware suppliers are trying to widen their role across research and commercial AI at the same time. Software is projected to grow at a 23.16% CAGR from 2026 to 2031, which makes it the fastest-growing component and points to increasing value in orchestration frameworks, schedulers, abstraction layers, and memory management tools. Services continue to matter because many customers still need integration support to operate mixed environments with fewer deployment errors. Within the heterogeneous computing industry, this mix suggests that hardware is still the entry point for spending, while software and services are becoming the main path to recurring revenue and tighter customer retention.

On-premises accounted for 50.48% of the heterogeneous computing market in 2025, underscoring that local infrastructure remained the largest deployment base despite heavy cloud investment. That position reflected the installed footprint of enterprise GPU clusters, university systems, and national laboratory environments that had already absorbed several years of capital spending. The segment also benefited from practical constraints, since real-time inference workloads often require lower latency than distant cloud regions can consistently deliver. Data control requirements added further support for local deployment, especially in finance, healthcare, and government settings, where model weights, training data, and sensitive outputs are subject to stricter handling rules.

Cloud is projected to grow at a 22.78% CAGR through 2031, making it the fastest-growing deployment mode in the heterogeneous computing market. This growth is being driven by hyperscaler investment in mixed GPU and ASIC environments that allow customers to access specialized processors without upfront capital commitments. Hybrid deployment is expanding beyond that trend, as enterprises route workloads between local and cloud resources based on latency, compliance, and cost conditions rather than using one environment for every task. The coexistence of a leading on-premises base and a faster-growing cloud segment suggests that the heterogeneous computing market is not following a one-way migration path. Instead, workload placement is becoming more selective, which sustains demand for deployment flexibility across the broader heterogeneous computing industry.

Complete Report Scope:

• By Component
  • Hardware
  • Software
  • Services
• By Deployment Mode
  • On-Premises
  • Cloud
  • Hybrid
• By Processor Type
  • Central Processing Unit (CPU)
  • Graphics Processing Unit (GPU)
  • Field-Programmable Gate Array (FPGA)
  • Application-Specific Integrated Circuit (ASIC)
  • Other Processor Types
• By Application
  • Artificial Intelligence and Machine Learning
  • Data Center and Cloud Computing
  • Scientific Simulation and Modeling
  • Edge Computing
  • Automotive and Autonomous Systems
  • Other Applications
• By End User
  • Enterprises
  • Government and Public Sector
  • Research Institutes and Academia
  • Telecommunications and Network Operators
  • Other End Users
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 40.83% of the heterogeneous computing market in 2025, and that lead rested on its deep concentration of hyperscale data centers, advanced AI developers, and defense-linked computing programs. The United States remained the core of this position because its policy stance and commercial infrastructure continued to support the fast deployment of AI clusters and related semiconductor capacity. The January 2025 executive order on advancing U.S. leadership in AI infrastructure showed that federal policy was already aligning build-out speed, supply security, and permitting direction around large-scale compute expansion. Canada strengthened the regional picture in April 2026 by launching its AI Sovereign Compute Infrastructure Program to support Canadian-owned AI-optimized high-performance systems. In the heterogeneous computing market, that combination of private scale and public support kept North America ahead on both installed capacity and near-term procurement momentum.

Asia-Pacific is projected to grow at a 22.36% CAGR through 2031, making it the fastest-growing region in the heterogeneous computing market. The region's growth is being supported by government compute programs, rising domestic chip ambitions, and its central role in memory, packaging, and broader semiconductor supply chains. This matters because many of the core enabling technologies for heterogeneous systems, including advanced packaging and high-bandwidth memory, are concentrated in Asia-Pacific production networks. That concentration can speed deployment for local buyers while also tying regional growth to global demand for accelerators, servers, and supporting components. The heterogeneous computing market is therefore likely to see Asia-Pacific strengthen both as a demand center and as a supply-side backbone for future system scaling.

Europe remained an important part of the heterogeneous computing market because public policy and industrial modernization are pushing AI infrastructure into more sectors. The United Kingdom's June 2026 AI Hardware Plan stood out because it paired compute capacity spending with support for domestic chip firms and technical skills development. South America and Middle East and Africa were earlier-stage regions in 2026, but the market direction there was still improving as governments and institutions evaluated sovereign compute capacity and local AI infrastructure needs. These regions face higher barriers around capital intensity, power systems, and engineering availability, which means adoption may move in steps rather than at the same pace seen in North America or Asia-Pacific. Even so, the heterogeneous computing market has room to deepen in these regions when public programs, telecom modernization, and enterprise AI deployment become more coordinated.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Qualcomm Incorporated
5. Broadcom Inc.
6. Marvell Technology, Inc.
7. MediaTek Inc.
8. Arm Holdings plc
9. Samsung Electronics Co., Ltd.
10. Huawei Technologies Co., Ltd.
11. Graphcore Limited
12. Cerebras Systems, Inc.
13. Groq, Inc.
14. Tenstorrent Inc.
15. SiFive, Inc.
16. Cambricon Technologies Corporation Limited
17. Biren Technology (Shanghai) Co., Ltd.
18. Ampere Computing LLC
19. SambaNova Systems, Inc.
20. Untether AI Corp.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rising Generative AI and Large Language Model Workloads
  • 4.2.2 Growing Need for Parallel Processing in AI Training and Inference
  • 4.2.3 Expansion of Sovereign AI and National Compute Infrastructure
  • 4.2.4 Shift Toward Energy-Efficient and Performance-per-Watt Architectures
  • 4.2.5 Growth of Multimodal and Real-Time Analytics Workloads
  • 4.2.6 Edge-to-Cloud Orchestration for Low-Latency Compute
• 4.3 Market Restraints
  • 4.3.1 High Capital Intensity for Heterogeneous System Design and Validation
  • 4.3.2 Scarcity of Parallel Programming and Hardware-Software Co-Design Talent
  • 4.3.3 Thermal and Power Delivery Constraints in Dense Compute Environments
  • 4.3.4 Supply Chain Dependence on Advanced Nodes, HBM, and Packaging Capacity
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Impact of Macroeconomic Factors on the Market
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Bargaining Power of Buyers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Hardware
  • 5.1.2 Software
  • 5.1.3 Services
• 5.2 By Deployment Mode
  • 5.2.1 On-Premises
  • 5.2.2 Cloud
  • 5.2.3 Hybrid
• 5.3 By Processor Type
  • 5.3.1 Central Processing Unit (CPU)
  • 5.3.2 Graphics Processing Unit (GPU)
  • 5.3.3 Field-Programmable Gate Array (FPGA)
  • 5.3.4 Application-Specific Integrated Circuit (ASIC)
  • 5.3.5 Other Processor Types
• 5.4 By Application
  • 5.4.1 Artificial Intelligence and Machine Learning
  • 5.4.2 Data Center and Cloud Computing
  • 5.4.3 Scientific Simulation and Modeling
  • 5.4.4 Edge Computing
  • 5.4.5 Automotive and Autonomous Systems
  • 5.4.6 Other Applications
• 5.5 By End User
  • 5.5.1 Enterprises
  • 5.5.2 Government and Public Sector
  • 5.5.3 Research Institutes and Academia
  • 5.5.4 Telecommunications and Network Operators
  • 5.5.5 Other End Users
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 India
    • 5.6.3.5 Southeast Asia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Vendor Positioning Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Advanced Micro Devices, Inc.
  • 6.4.3 Intel Corporation
  • 6.4.4 Qualcomm Incorporated
  • 6.4.5 Broadcom Inc.
  • 6.4.6 Marvell Technology, Inc.
  • 6.4.7 MediaTek Inc.
  • 6.4.8 Arm Holdings plc
  • 6.4.9 Samsung Electronics Co., Ltd.
  • 6.4.10 Huawei Technologies Co., Ltd.
  • 6.4.11 Graphcore Limited
  • 6.4.12 Cerebras Systems, Inc.
  • 6.4.13 Groq, Inc.
  • 6.4.14 Tenstorrent Inc.
  • 6.4.15 SiFive, Inc.
  • 6.4.16 Cambricon Technologies Corporation Limited
  • 6.4.17 Biren Technology (Shanghai) Co., Ltd.
  • 6.4.18 Ampere Computing LLC
  • 6.4.19 SambaNova Systems, Inc.
  • 6.4.20 Untether AI Corp.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment