과학 계산용 GPU : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the scientific computing GPU market size was valued at USD 7.92 billion in 2025 and estimated to grow from USD 9.98 billion in 2026 to reach USD 30.65 billion by 2031, at a CAGR of 25.16% during the forecast period (2026-2031).

This report is Segmented by GPU Architecture/Form Factor (Discrete Data Center and HPC GPUs, and More), Deployment Mode (On-Premises HPC and Research Infrastructure, Public Cloud, and More), Application (Quantum Simulation and Advanced Physics Research, and More), End-User (Research Institutions and Academia, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Scientific Computing GPU Market Trends and Insights

Rising Demand for GPU-Accelerated Scientific Simulation

GPU-accelerated simulation remains the main engine of demand in the scientific computing GPU market because large research programs still depend on high-precision modeling in fusion, materials, energy, and quantum work. The US Department of Energy announced more than USD 1 billion in public-private investment for the Lux and Discovery systems at Oak Ridge National Laboratory, underscoring how national programs are anchoring large purchase cycles. Research from Lawrence Livermore National Laboratory and collaborators showed that GB200 and GH200 GPUs delivered up to 59% performance gains on key finite element kernels and reduced energy use by up to 83%, strengthening the case for faster upgrades in the scientific computing GPU market. These gains matter because shorter run times let institutions move more models into production use instead of reserving GPUs for only the largest jobs. Buyers, therefore, want a single system that can support both double-precision simulation and AI-assisted modeling, which is changing how the scientific computing GPU market is specified in new procurements.

Growth in AI-Driven Research Workloads

AI-heavy research workloads are growing faster than simulation-only jobs inside the scientific computing GPU market, so buyers are now weighing AI throughput alongside FP64 performance. RIKEN deployed 1,600 NVIDIA Blackwell GPUs for AI-for-science applications spanning drug discovery, materials science, and climate modeling, which shows how national labs are building combined AI and HPC capacity. A September 2025 study in Nature Methods found that GPU-accelerated protein homology search reduced processing time by 6x compared to CPU systems while maintaining agreement with established benchmarks. NVIDIA launched the BioNeMo Agent Toolkit in 2026, and the platform is integrated into the workflows of Dassault Systemes, Cadence, and Schrodinger for drug discovery. As AI moves into the research loop rather than remaining at the analysis stage, the scientific computing graphics processing unit (GPU) market is shifting toward platforms that can run simulation, training, and inference on the same fabric.

High Upfront Cost of Scientific Computing GPU Infrastructure

High upfront cost still limits adoption in the scientific computing GPU market, especially outside national labs, top pharmaceutical companies, and the largest research universities. Supermicro said a Vera Rubin NVL4 rack-scale unit uses 1,152 GPUs and is rated at 362 kW, which shows that power and cooling upgrades are often as important as the chips themselves. Smaller institutions in South America, the Middle East, Africa, and parts of Europe often lack the budget or facility headroom for this level of density, which slows direct participation in the scientific computing GPU market. Public-private programs such as the US Department of Energy's Lux and Discovery projects are easing this barrier for some buyers, but these models remain concentrated in a limited number of countries. Until similar financing structures spread more widely, growth will continue to favor institutions that can fund both compute hardware and the supporting facility rebuild.

Other drivers and restraints analyzed in the detailed report include:

1. Expanding Use of Cloud-Based GPU Access for Research Institutions
2. Increasing Adoption of Multi-GPU and Heterogeneous Compute Clusters
3. Advanced Packaging and HBM Supply Concentration

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

Segment Analysis

Discrete Data Center and HPC GPUs held 87.32% of the scientific computing GPU market share in 2025, which kept this category at the center of production research deployments. This position reflects the large installed base of NVIDIA H100, GH200, and GB200 systems, as well as AMD MI300X and MI355X platforms already embedded in global HPC sites. The scientific computing GPU market still favors this class because most simulation codes have been tuned for discrete-accelerator environments for many years, making migration slow and expensive for institutions with deep software portfolios. That installed base effect is reinforced by mature programming stacks, where vendor software environments continue to anchor workflow stability for major research users.

GPU-Based Heterogeneous Accelerators are projected to grow at a 25.96% CAGR through 2031, which makes them the most dynamic architecture segment in the scientific computing GPU market. EuroHPC's Alice Recoque system, contracted in November 2025, specified AMD MI430X GPUs, AMD Venice processors, and SiPearl Rhea2 processors in a coherent-memory design, demonstrating that mixed architectures are becoming a baseline requirement in flagship systems. The smaller, integrated, and specialized accelerator segments remain relevant when edge instruments, low-latency inference, or unusual dataflow patterns make standard racks less efficient, and Sandia-backed research on the Cerebras Wafer-Scale Engine showed a 179-fold improvement in molecular dynamics timesteps per second compared with the Frontier platform for a targeted workload. This is widening the scientific computing GPU market from a single-architecture purchase decision into a portfolio approach, where facilities combine discrete GPUs with niche accelerators for specific tasks.

On-Premises HPC and Research Infrastructure held a 46.89% share in 2025, indicating that direct control over systems, data, and interconnect design still matters in the scientific computing GPU market. National laboratories, government research centers, and major universities continue to prefer dedicated hardware because they run predictable workloads and often handle restricted or sensitive data. The scientific computing GPU market still leans toward on-site systems where customized networking, security rules, and workload tuning go beyond standard cloud templates. At the same time, next-generation rack densities are making it harder for older university facilities to host the newest platforms, which is raising interest in hosted and colocation models.

Public Cloud is projected to expand at a 26.13% CAGR through 2031, the fastest pace across deployment modes in the scientific computing GPU market. AWS added support for P6e-GB200 and P6e-GB300 UltraServer models in June 2026, giving research users access to up to 72 NVIDIA Blackwell GPUs in a single NVLink domain via a managed service. NVIDIA also said major cloud providers will be among the first to deploy Vera Rubin-based instances in the second half of 2026, which extends advanced capacity to institutions that cannot buy the newest systems outright. Hybrid and multi-cloud use is therefore becoming a practical risk-control model, with buyers splitting workloads between internal clusters and outside capacity to reduce delays and keep projects moving.

Complete Report Scope:

• By GPU Architecture / Form Factor
  • Discrete Data Center and HPC GPUs
  • Integrated and SoC GPUs
  • GPU-Based Heterogeneous Accelerators
  • Other Specialized Scientific Computing GPUs
• By Deployment Mode
  • On-Premises HPC and Research Infrastructure
  • Public Cloud
  • Hosted / Colocation HPC Infrastructure
  • Edge and On-Instrument Deployment
  • Hybrid and Multi-Cloud Deployment
• By Application
  • Numerical Simulation and Computational Modeling
  • AI and Machine Learning for Scientific Discovery
  • High-Performance Data Analytics and Scientific Visualization
  • Life Sciences and Bioinformatics Computing
  • Quantum Simulation and Advanced Physics Research
  • Other Scientific Computing Applications
• By End-User
  • Research Institutions and Academia
  • Government Laboratories and National Research Centers
  • Defense, Aerospace, and Space Organizations
  • Healthcare, Pharmaceutical, and Life Sciences
  • Manufacturing and Industrial R&D
  • Information Technology, Cloud Service Providers, and Telecommunications
  • Financial Services and Quantitative Research
  • 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 48.23% of the scientific computing GPU market share in 2025, maintaining the region's clear lead. US Department of Energy procurements at Argonne and Oak Ridge created a large public-sector demand anchor through Solstice, Lux, and Discovery. Pharmaceutical investment added a second stream, with Eli Lilly and Roche both scaling sizable GPU programs in early 2026. This combination of federal science spending, university research capacity, and commercial drug development keeps North America at the center of the scientific computing GPU market.

Europe remained the second-largest region in 2025, and the scientific computing GPU market there is being shaped by coordinated procurement under the EuroHPC Joint Undertaking. JUPITER, equipped with around 24,000 NVIDIA GH200 Grace Hopper Superchips, ranked 4th on the June 2025 TOP500 list and became Europe's first exascale-class system. The Alice Recoque contract added a second major step by combining AMD GPUs with SiPearl processors, reflecting Europe's effort to broaden supply options while maintaining strong local system participation. Funding commitments, such as the UK's GBP 750 million (USD 945 million) investment in a national supercomputer and the SEANERGYS program, show that performance and energy efficiency are advancing together in regional planning.

Asia-Pacific is projected to expand at a 26.09% CAGR through 2031, making it the fastest-growing regional block in the scientific computing GPU market. Japan is driving this growth through RIKEN's deployment of 2,140 NVIDIA Blackwell GPUs across new AI-for-science and quantum systems, while FugakuNEXT remains under joint design for a later step-change in performance. China is following a different path under export controls, and the National Supercomputing Center in Shenzhen announced the 2 exaflops LineShine system built on domestic Huawei LX2 processors without GPU accelerators. These approaches show that the scientific computing graphics processing unit (GPU) market in Asia-Pacific is splitting between open global procurement, as seen in Japan, and sovereign substitution programs, as seen in China. South America, the Middle East, and Africa, and smaller Asia-Pacific markets, remain more dependent on cloud access than on local exascale infrastructure, which keeps adoption moving but at a different scale than in the leading regions.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Qualcomm Incorporated
5. Imagination Technologies Limited
6. Arm Limited
7. Matrox Electronic Systems Ltd.
8. ASPEED Technology Inc.
9. Biren Technology (Hong Kong) Co., Limited
10. Moore Threads Intelligent Technology (Beijing) Co., Ltd.
11. Shanghai Jingjia Microelectronics Co., Ltd.
12. Innosilicon Technology (Shenzhen) Co., Ltd.
13. VeriSilicon Microelectronics (Shanghai) Co., Ltd.
14. Graphcore Limited
15. Tenstorrent Inc.
16. Cerebras Systems, Inc.
17. SambaNova Systems, Inc.
18. d-Matrix, Inc.
19. SiPearl SAS
20. Peak Energy, Inc.

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 Demand for GPU-Accelerated Scientific Simulation
  • 4.2.2 Growth in AI-Driven Research Workloads
  • 4.2.3 Expanding Use of Cloud-Based GPU Access for Research Institutions
  • 4.2.4 Increasing Adoption of Multi-GPU and Heterogeneous Compute Clusters
  • 4.2.5 Export-Control-Led Reconfiguration of High-End Compute Supply Chains
  • 4.2.6 Power-Efficiency Pressure in National Supercomputing Programs
• 4.3 Market Restraints
  • 4.3.1 High Upfront Cost of Scientific Computing GPU Infrastructure
  • 4.3.2 Advanced Packaging and HBM Supply Concentration
  • 4.3.3 Cooling, Power Delivery, and Rack Density Constraints
  • 4.3.4 Software Portability and Kernel Optimization Friction Across GPU Architectures
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 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 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By GPU Architecture / Form Factor
  • 5.1.1 Discrete Data Center and HPC GPUs
  • 5.1.2 Integrated and SoC GPUs
  • 5.1.3 GPU-Based Heterogeneous Accelerators
  • 5.1.4 Other Specialized Scientific Computing GPUs
• 5.2 By Deployment Mode
  • 5.2.1 On-Premises HPC and Research Infrastructure
  • 5.2.2 Public Cloud
  • 5.2.3 Hosted / Colocation HPC Infrastructure
  • 5.2.4 Edge and On-Instrument Deployment
  • 5.2.5 Hybrid and Multi-Cloud Deployment
• 5.3 By Application
  • 5.3.1 Numerical Simulation and Computational Modeling
  • 5.3.2 AI and Machine Learning for Scientific Discovery
  • 5.3.3 High-Performance Data Analytics and Scientific Visualization
  • 5.3.4 Life Sciences and Bioinformatics Computing
  • 5.3.5 Quantum Simulation and Advanced Physics Research
  • 5.3.6 Other Scientific Computing Applications
• 5.4 By End-User
  • 5.4.1 Research Institutions and Academia
  • 5.4.2 Government Laboratories and National Research Centers
  • 5.4.3 Defense, Aerospace, and Space Organizations
  • 5.4.4 Healthcare, Pharmaceutical, and Life Sciences
  • 5.4.5 Manufacturing and Industrial R&D
  • 5.4.6 Information Technology, Cloud Service Providers, and Telecommunications
  • 5.4.7 Financial Services and Quantitative Research
  • 5.4.8 Other End-Users
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 South Korea
    • 5.5.3.4 India
    • 5.5.3.5 Southeast Asia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market 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 Imagination Technologies Limited
  • 6.4.6 Arm Limited
  • 6.4.7 Matrox Electronic Systems Ltd.
  • 6.4.8 ASPEED Technology Inc.
  • 6.4.9 Biren Technology (Hong Kong) Co., Limited
  • 6.4.10 Moore Threads Intelligent Technology (Beijing) Co., Ltd.
  • 6.4.11 Shanghai Jingjia Microelectronics Co., Ltd.
  • 6.4.12 Innosilicon Technology (Shenzhen) Co., Ltd.
  • 6.4.13 VeriSilicon Microelectronics (Shanghai) Co., Ltd.
  • 6.4.14 Graphcore Limited
  • 6.4.15 Tenstorrent Inc.
  • 6.4.16 Cerebras Systems, Inc.
  • 6.4.17 SambaNova Systems, Inc.
  • 6.4.18 d-Matrix, Inc.
  • 6.4.19 SiPearl SAS
  • 6.4.20 Peak Energy, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment