휴머노이드 로봇 GPU 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the humanoid robot GPU market size is expected to increase from USD 96.82 million in 2025 to USD 203.91 million in 2026 and reach USD 978.32 million by 2031, growing at a CAGR of 36.84% over 2026-2031.

This report is Segmented by GPU Type (Data Center Training GPUs, Edge AI GPUs, Embedded GPUs, and Integrated GPU Platforms), Deployment Type (Onboard Compute, Hybrid Compute, and More), GPU Function (Motion Planning and Control, and More), Robot Capability (Dexterous Manipulation, and More), End Use Industry (Automotive, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Humanoid Robot GPU Market Trends and Insights

Rising Physical AI Compute Intensity in Humanoid Robots

The humanoid robot GPU market is expanding because physical AI workloads now run multiple model layers simultaneously rather than relying on a single vision or control task. NVIDIA positioned the Jetson AGX Thor T5000 to address this need with 2,070 FP4 teraflops, a significant leap from the Jetson AGX Orin platform and a clear indication of how quickly compute requirements are rising. The same GPU market for humanoid robots is also being shaped by memory requirements, as NVIDIA listed 24GB and higher VRAM for training systems and 128GB unified memory for edge inference in its GR00T hardware guidance. That matters because a developer moving from narrow imitation tasks to broader generalist behavior has to scale training hardware and onboard inference hardware simultaneously. NVIDIA also framed cloud-to-robot computing for physical AI as a foundational layer for humanoid development, which supports continued demand across both data center and embedded GPU products.

Growing Demand for Onboard Real-Time Inference

The humanoid robot GPU market is shifting toward onboard inference because latency and data-handling limitations make continuous cloud dependence less practical in live operating environments. 1X Technologies stated in 2026 that Jetson Thor was the only product available at the time that met the NEO robot's onboard compute requirement for real-time sensor processing, underscoring how narrow the field still is at the high end of embedded performance. Boston Dynamics also expanded its collaboration with NVIDIA to integrate Jetson Thor into Atlas, bringing server-class reasoning capability onto the robot itself rather than keeping the heavy workload offboard. In the humanoid robot GPU market, that architecture change matters because every additional robot deployed becomes a direct hardware sale rather than depending solely on centralized training clusters. As more deployments move into production lines and warehouse workflows, local inference is becoming a standard design requirement rather than an optional premium feature.

High Power Draw and Thermal Design Complexity

The humanoid robot GPU market still faces a direct operating constraint because onboard compute has to share limited battery capacity with locomotion, sensing, and actuation. NVIDIA developer discussions about Jetson Thor showed that even the SoC-level power breakdown is a practical challenge for thermal design teams, especially when developers try to model sustained performance within the module's configurable TDP range. NVIDIA also described its Isaac GR00T reference robot with a 15Ah and 0.972kWh battery and around 3 hours of operating life, which remains well below a full industrial shift and forces workarounds such as battery swaps or fixed power support. In the humanoid-robot GPU market, that power ceiling slows adoption because peak inference and actuator loads hit the same system simultaneously. It also favors larger vendors that can invest in integrated thermal management, power governors, and full-system optimization.

Other drivers and restraints analyzed in the detailed report include:

1. Increasing Use of Synthetic Data and Simulation Pipelines
2. Expanding Pilot Deployments in Automotive Manufacturing
3. Elevated Bill of Materials and Total Cost Of Ownership

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

Segment Analysis

Data Center Training GPUs held 64.92% of the humanoid robot GPU market share in 2025, indicating that most spending still sat upstream in model development rather than in fielded robots. The humanoid robot GPU market relied on that training layer because generalist robot policies need large-scale simulation, synthetic data generation, and continuous model refinement before commercial fleets can expand. NVIDIA's cloud-to-robot positioning for physical AI reflected this demand pattern by tying data center systems directly to robot training, simulation, and deployment workflows. The same humanoid robot GPU market also showed why embedded systems matter, as Jetson Orin had supported earlier deployments, and Jetson Thor moved into the role of reference onboard compute for more advanced commercial humanoids.

NVIDIA's ecosystem traction was reinforced by public commitments from Boston Dynamics and 1X, both of which tied their robot roadmaps to Jetson Thor for onboard reasoning and sensor processing. Integrated GPU Platforms are projected to grow at a 37.61% CAGR from 2026 to 2031, making them the fastest-growing segment of this market. The humanoid robot GPU market is moving in that direction because integrated platforms reduce board complexity and can better balance power, thermal load, and compute than purely discrete approaches in mobile robots. Qualcomm's robotics platform launch at CES 2026 reflected this shift with a design built around CPU, GPU, and AI acceleration in one architecture for humanoid and mobile robotics use. For the humanoid robot GPU industry, that means the next phase of competition is likely to center on full-stack efficiency rather than peak standalone compute alone.

Offboard Training and Simulation accounted for 65.38% of revenue in 2025, indicating that the humanoid robot GPU market remained focused on development infrastructure at that time. Vendors and robot developers still spent heavily on simulation clusters because digital environments let them test policies at a much larger scale than real-world trials can support. NVIDIA linked its humanoid development stack to Omniverse, Isaac, and Blackwell systems, which reflects how central offboard training remains in this market. The humanoid robot GPU market also continued to support hybrid models in which robots execute local inference while receiving heavier model updates from the cloud or data centers during downtime. That hybrid approach fits the current commercial stage because it lets operators use centralized training gains without forcing every compute step onto the robot.

Onboard Compute is forecast to expand at a 38.14% CAGR through 2031, which makes it the fastest-growing deployment mode in the humanoid robot GPU market. That acceleration follows from production settings in which latency, privacy, and operational continuity make constant offboard dependence hard to justify. 1X and Boston Dynamics both pointed to onboard Jetson Thor integration as the path to real-time reasoning and sensor processing on deployed robots, which gives this segment tangible commercial backing. As deployments widen, the humanoid robot GPU market is likely to move toward a more balanced split between centralized development compute and distributed embedded inference hardware.

Complete Report Scope:

• By GPU Type
  • Data Center Training GPUs
  • Edge AI GPUs
  • Embedded GPUs
  • Integrated GPU Platforms
• By Deployment Type
  • Onboard Compute
  • Offboard Training And Simulation
  • Hybrid Compute
• By GPU Function
  • Training and Simulation
  • Inference and Perception
  • Motion Planning and Control
  • Digital Twin and Synthetic Data Generation
• By Robot Capability
  • Real-Time Perception
  • Motion Planning and Control
  • Dexterous Manipulation
  • Multi-Modal Reasoning
• By End Use Industry
  • Automotive
  • Logistics and Warehousing
  • Manufacturing and Assembly
  • Research and Education
  • Healthcare and Assisted Living
  • Defense and Security
• 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

Asia-Pacific accounted for 47.62% of revenue in 2025, making it the largest region in the humanoid robot GPU market. That lead came from the concentration of humanoid OEM activity in China, the semiconductor base in Japan and South Korea, and broader public support for physical AI programs described in the draft. The humanoid robot GPU market in Asia-Pacific also benefits from a supply chain that can support sensors, packaging, memory, and compute integration at scale. Domestic compute platforms are beginning to supplement NVIDIA-based deployments in the region, which matters because localization goals are becoming a stronger factor in purchasing decisions. Even with that shift, the humanoid robot GPU market in Asia-Pacific remains closely tied to how quickly regional OEMs can move from pilot output toward repeatable commercial deployment.

North America is projected to expand at a 38.57% CAGR through 2031, making it the fastest-growing regional segment in the humanoid robot GPU market. The region combines a large installed base of AI infrastructure with several of the most commercially visible humanoid developers, which gives it a strong position in both training and deployment. NVIDIA's Jetson Thor roadmap and ecosystem messaging were directed heavily toward this development base, while Boston Dynamics and 1X both linked their robot stacks to NVIDIA's onboard compute path. The humanoid robot GPU market in North America is also supported by the Robots-as-a-Service model, which turns deployments into a recurring hardware and software demand stream instead of a one-time equipment sale. Agility Robotics' commercial agreement with Toyota Motor Manufacturing Canada shows how that model is moving into live industrial operations and supporting embedded GPU demand at the unit level.

Europe accounted for a significant share of 2025 revenue in the humanoid robot GPU market, led by Germany's automotive deployments and the region's broader industrial automation base. BMW's Leipzig program gave Europe a visible reference point for physical AI in automotive production and reinforced the region's role in early industrial adoption. South America and the Middle East and Africa remained smaller contributors, but the humanoid robot GPU market gained a clear South American entry point through Mercado Libre's agreement with Agility Robotics in late 2025. Across these regions, safety compliance and deterministic system behavior are likely to matter more as commercial deployments move closer to routine human-robot collaboration.

1. NVIDIA Corporation
2. Qualcomm Incorporated
3. Intel Corporation
4. Advanced Micro Devices, Inc.
5. Huawei Technologies Co., Ltd.
6. Tesla, Inc.
7. Baidu, Inc.
8. Horizon Robotics Inc.
9. Rockchip Electronics Co., Ltd.
10. MediaTek Inc.
11. Samsung Electronics Co., Ltd.
12. Alphabet Inc.
13. Amazon.com, Inc.
14. Foxconn Technology Co., Ltd.
15. Agility Robotics, Inc.
16. Figure AI, Inc.
17. Apptronik, Inc.
18. Boston Dynamics, Inc.
19. UBTECH Robotics Corp Ltd.
20. Unitree Robotics
21. Sanctuary Cognitive Systems Corporation
22. 1X Technologies AS
23. Fourier Intelligence Co., Ltd.
24. XPENG Inc.
25. Siemens AG
26. ABB Ltd.
27. KUKA AG

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 Physical AI Compute Intensity In Humanoid Robots
  • 4.2.2 Growing Demand For Onboard Real-Time Inference
  • 4.2.3 Increasing Use Of Synthetic Data And Simulation Pipelines
  • 4.2.4 Expanding Pilot Deployments In Automotive Manufacturing
  • 4.2.5 Rising Preference For Edge Processing To Reduce Latency And Cloud Dependency
  • 4.2.6 Strategic Platform Lock-In By Leading Robotics Compute Vendors
• 4.3 Market Restraints
  • 4.3.1 High Power Draw And Thermal Design Complexity
  • 4.3.2 Elevated Bill Of Materials And Total Cost Of Ownership
  • 4.3.3 Software Portability And Developer Ecosystem Fragmentation
  • 4.3.4 Limited Real-World Training Data For Edge Cases
• 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

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By GPU Type
  • 5.1.1 Data Center Training GPUs
  • 5.1.2 Edge AI GPUs
  • 5.1.3 Embedded GPUs
  • 5.1.4 Integrated GPU Platforms
• 5.2 By Deployment Type
  • 5.2.1 Onboard Compute
  • 5.2.2 Offboard Training And Simulation
  • 5.2.3 Hybrid Compute
• 5.3 By GPU Function
  • 5.3.1 Training and Simulation
  • 5.3.2 Inference and Perception
  • 5.3.3 Motion Planning and Control
  • 5.3.4 Digital Twin and Synthetic Data Generation
• 5.4 By Robot Capability
  • 5.4.1 Real-Time Perception
  • 5.4.2 Motion Planning and Control
  • 5.4.3 Dexterous Manipulation
  • 5.4.4 Multi-Modal Reasoning
• 5.5 By End Use Industry
  • 5.5.1 Automotive
  • 5.5.2 Logistics and Warehousing
  • 5.5.3 Manufacturing and Assembly
  • 5.5.4 Research and Education
  • 5.5.5 Healthcare and Assisted Living
  • 5.5.6 Defense and Security
• 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 Qualcomm Incorporated
  • 6.4.3 Intel Corporation
  • 6.4.4 Advanced Micro Devices, Inc.
  • 6.4.5 Huawei Technologies Co., Ltd.
  • 6.4.6 Tesla, Inc.
  • 6.4.7 Baidu, Inc.
  • 6.4.8 Horizon Robotics Inc.
  • 6.4.9 Rockchip Electronics Co., Ltd.
  • 6.4.10 MediaTek Inc.
  • 6.4.11 Samsung Electronics Co., Ltd.
  • 6.4.12 Alphabet Inc.
  • 6.4.13 Amazon.com, Inc.
  • 6.4.14 Foxconn Technology Co., Ltd.
  • 6.4.15 Agility Robotics, Inc.
  • 6.4.16 Figure AI, Inc.
  • 6.4.17 Apptronik, Inc.
  • 6.4.18 Boston Dynamics, Inc.
  • 6.4.19 UBTECH Robotics Corp Ltd.
  • 6.4.20 Unitree Robotics
  • 6.4.21 Sanctuary Cognitive Systems Corporation
  • 6.4.22 1X Technologies AS
  • 6.4.23 Fourier Intelligence Co., Ltd.
  • 6.4.24 XPENG Inc.
  • 6.4.25 Siemens AG
  • 6.4.26 ABB Ltd.
  • 6.4.27 KUKA AG

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment