로봇 및 스마트 제조용 GPU : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the GPU in robotics and smart manufacturing market size is expected to increase from USD 1.84 billion in 2025 to USD 2.25 billion in 2026 and reach USD 5.06 billion by 2031, growing at a CAGR of 22.42% over 2026-2031.

This report is Segmented by Application (Machine Vision and Quality Inspection, Autonomous and Collaborative Robots, and More), Robot Type (Industrial Robots, Collaborative Robots, and More), Deployment Environment (On-Premise Edge Systems, and More), End-User Industry (Automotive Manufacturing, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global GPU In Robotics and Smart Manufacturing Market Trends and Insights

Surge in AI-Driven Machine Vision Quality Inspection

Modern electronics and semiconductor lines process gigapixel images at full conveyor speed, a data rate that overwhelms CPU pipelines. New inspection platforms pair 200-megapixel sensors with tensor-core GPUs, cutting escape rates below five parts per million and shrinking feedback loops from end-of-line to within a single lot. Automotive tier-1 suppliers report 25-40% fewer false positives, saving up to USD 5 million per plant each year. Transformer vision networks, which demand roughly ten times more compute than legacy convolutional models, cement GPUs as the only viable inference engine for next-generation quality control.

Expanding Adoption of Collaborative Robots in Automotive and Electronics Factories

Retrofit accelerator cards now slide into existing cobots, bringing real-time force-torque sensing and adaptive grasping without replacing mechanical arms. Automotive battery lines need sub-millimeter alignment that cannot tolerate 100-millisecond cloud roundtrips, so embedded GPUs handle vision, safety and motion planning locally. Eliminating perimeter cages reduces installation costs by up to 40% and frees floor space for additional tooling, a compelling return on investment for plants juggling frequent model changes.

High Upfront Cost and Total Cost of Ownership of Industrial GPU Systems

A rugged four-GPU edge box with liquid cooling can list above USD 100,000, quadruple the price of a CPU automation controller. Chilled-water loops, software licenses and IEC 62443 cybersecurity add another 20-30% over five years. Small and medium manufacturers in cost-sensitive economies postpone upgrades despite attractive payback windows, limiting near-term penetration outside tier-1 enterprises.

Other drivers and restraints analyzed in the detailed report include:

1. Demand for Real-Time Predictive Maintenance Powered by Edge GPUs
2. Rising Investment in Industry 4.0 Smart Factories Across Asia-Pacific
3. Integration Complexity with Legacy PLC and Control Architectures

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

Segment Analysis

GPU in robotics and smart manufacturing market size allocation shows machine vision claimed 38% of 2025 revenue. Semiconductor fabs demand sub-micron defect detection, locking GPUs into every inspection bay. Digital twin and simulation workloads are the fastest climber because automotive OEMs now test dozens of line layouts in virtual environments, compressing ramp-up schedules by half. Predictive maintenance steadily scales as edge servers handle 1 kHz sensor fusion without overspending on compute. Autonomous material handling remains the smallest slice but is poised to surge as e-commerce hubs retrofit fleets of mobile robots that require sub-50-millisecond local inference.

Digital-twin users note energy savings up to 12% and cycle-time cuts near 8% after optimizing paint-shop robotics on high-end GPU clusters. Transformer vision models, ten times heavier than previous CNNs, guarantee continued silicon demand. Predictive maintenance users report downtime reductions of 25-40%, translating to millions of dollars annually. Autonomous material handling is gaining traction because an AMR fleet of 200-plus units creates larger cumulative GPU demand than incremental arm upgrades on fixed robots.

Industrial robots delivered 49% of 2025 revenue for the GPU in robotics and smart manufacturing market, driven by established automotive and electronics programs. AMRs and AGVs, though smaller today, are expanding quickest because warehouse paths change second-by-second and cannot wait for cloud decisions. Collaborative robots trail in share but show double-digit growth as bolt-on GPU cards modernize installed fleets. New cobot models integrate wrist cameras and embedded GPUs that moderate grip force, trimming scrap on fragile components by up to 50%.

North American warehouses added more than 1,000 new AMRs in early 2026, proving that edge GPUs erase the 100-200 ms latency that once capped robot speed. Industrial arms now rely on GPU-guided bin picking to achieve 120 picks per minute, aligning with just-in-sequence automotive delivery. Collaborative units benefit from GPU-accelerated safety perception that allows human-robot co-work without expensive cages.

Complete Report Scope:

• By Application
  • Machine Vision and Quality Inspection
  • Autonomous and Collaborative Robots (Cobots)
  • Industrial AI and Predictive Maintenance
  • Digital Twin and Simulation
  • Autonomous Material Handling
• By Robot Type
  • Industrial Robots
  • Collaborative Robots (Cobots)
  • Autonomous Mobile Robots (AMRs/AGVs)
• By Deployment Environment
  • On-Premise Edge Systems
  • Cloud-Connected Smart Factories
  • Hybrid
• By End-User Industry
  • Automotive Manufacturing
  • Electronics and Semiconductor
  • Heavy Machinery and Industrial
  • Logistics and Warehousing
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

Asia-Pacific contributed 64% of 2025 revenue to the GPU in robotics and smart manufacturing market due to massive policy-backed rollouts in South Korea, China and Japan. South Korea's consortia plan 260,000 GPUs by 2027, while China mandates AI quality inspection in 30,000 smart factories. Japan subsidizes precision-machining SMEs that deploy GPU vision systems, and India includes GPUs in production-linked incentives for electronics clusters.

North America is the fastest-growing region over 2026-2031, buoyed by USD 202 billion of semiconductor and EV investments that specify GPU-accelerated defect detection. Arizona hosts multibillion-dollar fabs that embed GPU optics, and Tennessee's new EV campus will run 1 200 cobots with on-board inference. Mexico upgrades nearshored automotive lines with GPU vision to match U.S. throughput, lifting Latin American adoption from a small base.

Europe ranks third but gains momentum from an industrial AI cloud launched in Germany with 10 000 latest-generation GPUs. The European Union's AI Factories initiative allocates EUR 20 billion (USD 22 billion) for processors across Gigafactories, expanding demand for liquid-cooled server enclosures. The Middle East and Africa host early pilots in petrochemicals and logistics, while South America sees initial traction in automotive clusters.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Dell Technologies Inc.
5. Hewlett Packard Enterprise Company
6. Super Micro Computer, Inc.
7. ADLINK Technology Inc.
8. Advantech Co., Ltd.
9. Aetina Corporation
10. Neousys Technology Inc.
11. Kontron AG
12. Siemens AG
13. ABB Ltd.
14. Fanuc Corporation
15. Yaskawa Electric Corporation
16. KUKA AG
17. Universal Robots A/S
18. Boston Dynamics, Inc.
19. Geekplus Technology Co., Ltd.

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 Surge in AI-Driven Machine Vision Quality Inspection
  • 4.2.2 Expanding Adoption of Collaborative Robots in Automotive and Electronics Factories
  • 4.2.3 Demand for Real-Time Predictive Maintenance Powered by Edge GPUs
  • 4.2.4 Rising Investment in Industry 4.0 Smart Factories Across Asia-Pacific
  • 4.2.5 Availability of Compact Liquid-Cooled 4-GPU Edge Servers Enabling In-Cell Deployment
  • 4.2.6 Quantized Visual-Language-Action Models Enabling On-Robot Inference on Consumer GPUs
• 4.3 Market Restraints
  • 4.3.1 High Upfront Cost and Total Cost of Ownership of Industrial GPU Systems
  • 4.3.2 Integration Complexity with Legacy PLC and Control Architectures
  • 4.3.3 Thermal Management Challenges in Enclosed Robot Bases
  • 4.3.4 Supply-Chain Risks for Advanced Packaging Substrates in HBM-Based GPUs
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry
• 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE, USD)

• 5.1 By Application
  • 5.1.1 Machine Vision and Quality Inspection
  • 5.1.2 Autonomous and Collaborative Robots (Cobots)
  • 5.1.3 Industrial AI and Predictive Maintenance
  • 5.1.4 Digital Twin and Simulation
  • 5.1.5 Autonomous Material Handling
• 5.2 By Robot Type
  • 5.2.1 Industrial Robots
  • 5.2.2 Collaborative Robots (Cobots)
  • 5.2.3 Autonomous Mobile Robots (AMRs/AGVs)
• 5.3 By Deployment Environment
  • 5.3.1 On-Premise Edge Systems
  • 5.3.2 Cloud-Connected Smart Factories
  • 5.3.3 Hybrid
• 5.4 By End-User Industry
  • 5.4.1 Automotive Manufacturing
  • 5.4.2 Electronics and Semiconductor
  • 5.4.3 Heavy Machinery and Industrial
  • 5.4.4 Logistics and Warehousing
• 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 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 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 Share 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 Dell Technologies Inc.
  • 6.4.5 Hewlett Packard Enterprise Company
  • 6.4.6 Super Micro Computer, Inc.
  • 6.4.7 ADLINK Technology Inc.
  • 6.4.8 Advantech Co., Ltd.
  • 6.4.9 Aetina Corporation
  • 6.4.10 Neousys Technology Inc.
  • 6.4.11 Kontron AG
  • 6.4.12 Siemens AG
  • 6.4.13 ABB Ltd.
  • 6.4.14 Fanuc Corporation
  • 6.4.15 Yaskawa Electric Corporation
  • 6.4.16 KUKA AG
  • 6.4.17 Universal Robots A/S
  • 6.4.18 Boston Dynamics, Inc.
  • 6.4.19 Geekplus Technology Co., Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment