ヒューマノイドロボット向けGPU市場シェア分析、業界動向と統計、成長予測 2026-2031年

ヒューマノイドロボット向けGPU市場の規模は、2025年の9,682万米ドルから2026年には2億391万米ドルへと拡大し、2031年には9億7,832万米ドルに達すると予測されています。2026年から2031年にかけての年平均成長率（CAGR）は36.84%に達するとMordor Intelligenceでは予測しています。

2026年に見られる急激な市場拡大は、ロボット開発プログラムが試験環境から実際の生産現場へと移行する中で、試作段階の調達から初期の商用導入へと動きが変化していることを反映しています。ヒューマノイドシステムにおいて、認識、動作計画、言語ベースの推論を同時に実行する必要性が高まっていることから需要が増加しており、その結果、高帯域幅かつ低遅延なGPU演算機能が、構成部品（BOM）の重要な要素となっています。また、中国、日本、欧州におけるフィジカルAI（物理世界で動作するAI）プログラムへの公的支援も、導入までの期間を短縮し、現地での演算スタック（ハードウェア・ソフトウェア基盤）への投資拡大を後押ししています。ベンダー間の競争は、単体チップの性能よりも、ハードウェアとソフトウェアを統合した完全なエコシステムの優劣によって左右されるようになっています。そのため、ロボット開発者が特定の学習・推論スタックを採用すると、その後の切り替えコストが高くなる傾向があります。一方で、バッテリー容量や熱設計上の制約から、商用導入の成否は演算効率に大きく依存しており、性能、消費電力、システム統合のバランスを最適化できるサプライヤーが優位に立つ状況となっています。

レポートの主なポイント

• GPUタイプ別では、2025年のヒューマノイドロボット用GPU市場において「データセンター向け学習用GPU」が64.92%のシェアを占め、「統合型GPUプラットフォーム」は2031年まで年平均成長率（CAGR）37.61%で拡大すると予測されています。
• 導入形態別では、2025年に「オフボード学習・シミュレーション」が65.38%のシェアを占め、「オンボード・コンピューティング」は2031年までCAGR 38.14%で成長すると見込まれています。
• GPU機能別では、2025年の市場規模において「学習・シミュレーション」が48.87%を占め、「デジタルツインおよび合成データ生成」は2031年までCAGR 38.26%で成長すると予測されています。
• ロボットの機能別では、2025年に「リアルタイム認識」が35.63%で最大のシェアを占め、「マルチモーダル推論」は2031年までCAGR 37.92%で拡大すると見込まれています。
• エンドユース産業別では、2025年に「自動車」が32.51%のシェアで首位となり、「製造・組立」は2031年までCAGR 38.49%で成長すると予測されています。
• 地域別では、2025年のヒューマノイドロボット用GPU市場において「アジア太平洋地域」が47.62%のシェアを占め、「北米」は2031年までCAGR 38.57%で成長すると予測されています。

ロボットの機能別：認識機能が基盤を支える一方、マルチモーダル推論が計算負荷を増大させる

2025年時点では「リアルタイム認識」が35.63%という最大のシェアを占めました。これは、ヒューマノイドロボット向けGPU市場が、依然として物体認識、シーン認識、障害物回避といった処理（ワークロード）を主軸としていることを反映しています。産業現場への導入において認識機能が重視され続けているのは、これらのタスクが、構造化された環境下で実用レベルの信頼性に最初に到達する機能だからです。例えば、BMWのライプツィヒ工場に導入されたHexagon Robotics社の「AEON」は、カメラ、レーダー、力・トルクセンシングを含む21個のセンサー群を搭載していました。これは、実際の産業現場で稼働するヒューマノイドロボットが、組み込み型コンピューティングシステムに対して継続的な認識処理の負荷をかけていることを示しています。したがって、ヒューマノイドロボット向けGPU市場では、より高度な自律性が普及する前の段階として、認識機能があらゆる商用システムに不可欠な基本機能と位置づけられています。より高度なセンシングによってロボットがリアルタイムで精密な動作（ファインモータータスク）を実行できるようになるため、動作計画や巧みなマニピュレーション（器用な操作）も、この認識機能の領域と密接に関連しています。

「マルチモーダル推論」は、2031年まで年平均成長率（CAGR）37.92%で拡大すると予測されており、最も急成長する機能分野となっています。自然言語の解釈、新しい環境への適応、そして手作業による度重なる再プログラミングなしでの多段階タスクの遂行が可能なロボットへの需要が高まっているため、ヒューマノイドロボット向けGPU市場はこの方向へと動いています。NVIDIAの「GR00T N1.7」アップデート（アーリーアクセス版）では、接触を伴う複雑なタスクに対応するための、指レベルでの巧みな制御機能が追加されました。これは、認識、推論、マニピュレーションを同一システム内で統合し、より高度な能力を持つ制御ポリシー（動作指針）へと移行する動きを示唆しています。こうした機能が成熟するにつれ、ヒューマノイドロボット向けGPU市場では、より大規模なリアルタイムモデルをサポートできる十分なメモリと帯域幅を備えた、より強力なオンボードモジュールが求められるようになるでしょう。

Humanoid Robot GPU Market Analysis by 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. The sharp increase in 2026 reflects the shift from prototype procurement to early commercial deployments, as robot programs move from testing environments into live production settings. Demand is rising because humanoid systems now need to run perception, motion planning, and language-driven reasoning simultaneously, making high-bandwidth, low-latency GPU compute a core part of the bill of materials. Public support for physical AI programs in China, Japan, and Europe is also shortening adoption timelines and encouraging broader investment in local compute stacks. Vendor competition is being shaped less by standalone chips and more by complete hardware and software ecosystems, which raises switching costs once a robotics developer commits to a training and inference stack. At the same time, battery limits and thermal design constraints keep commercial adoption tied to compute efficiency, which gives an advantage to suppliers that can balance performance, power draw, and integration.

Key Report Takeaways

• By GPU type, Data Center Training GPUs held 64.92% of the humanoid robot GPU market share in 2025, while Integrated GPU Platforms are projected to expand at a 37.61% CAGR through 2031.
• By deployment type, Offboard Training and Simulation commanded 65.38% share in 2025, while Onboard Compute is expected to grow at a CAGR of 38.14% through 2031.
• By GPU function, Training and Simulation accounted for 48.87% of the humanoid robot GPU market size in 2025, while Digital Twin and Synthetic Data Generation are projected to grow at a 38.26% CAGR through 2031.
• By robot capability, Real-Time Perception held the largest share at 35.63% in 2025, while Multi-Modal Reasoning is expected to expand at a 37.92% CAGR through 2031.
• By end use industry, Automotive led with a 32.51% share in 2025, while Manufacturing and Assembly is projected to advance at a 38.49% CAGR through 2031.
• By geography, Asia-Pacific held 47.62% of the humanoid robot GPU market in 2025, while North America is projected to grow a 38.57% CAGR through 2031.

Note: Market size and forecast figures in this report are generated using Mordor Intelligence’s proprietary estimation framework, updated with the latest available data and insights as of January 2026.

Global Humanoid Robot GPU Market Trends and Insights

Drivers Impact Analysis*

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.

Increasing Use of Synthetic Data and Simulation Pipelines

The humanoid robot GPU market is gaining another demand layer from synthetic data generation, as real-world demonstration data remains limited and expensive to scale. NVIDIA reported that its Isaac GR00T blueprint generated 780,000 synthetic trajectories in 11 hours, which it equated to 6,500 hours of human demonstration data, and the company said the mixed dataset improved GR00T N1 policy performance by 40%.[1]This means the humanoid robot GPU market is no longer driven solely by robot inference, since training teams also need dense simulation clusters capable of running digital twins and world models at high volume. NVIDIA linked that expansion directly to its Blackwell systems and broader physical AI stack, reinforcing the split between data center GPUs for development and edge modules for deployment. Peer-reviewed robotics work also showed 64 NVIDIA L40 GPUs training across 1,024 simulated environments per GPU, which confirms that large-scale simulation is becoming a standard workload rather than a niche experiment.

Expanding Pilot Deployments in Automotive Manufacturing

The humanoid robot GPU market is being pushed forward by automotive programs, as that sector is now producing the clearest evidence that humanoids can add value within structured industrial workflows. BMW stated that its Figure AI pilot in Spartanburg supported the production of more than 30,000 BMW X3 vehicles over 10 months, with the robots accumulating 1,250 operating hours and handling more than 90,000 sheet-metal parts. Agility Robotics also announced a commercial Robots-as-a-Service agreement with Toyota Motor Manufacturing Canada in February 2026, which moved humanoid deployment from trial mode toward contracted production use. In the humanoid robot GPU market, these programs matter because they create repeat demand for both onboard inference hardware and the training infrastructure required for fleet updates. They also give other manufacturers a clearer benchmark for how quickly GPU-backed robot programs can move from pilot lines to broader plant operations.

Restraints Impact Analysis*

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.

Elevated Bill of Materials and Total Cost Of Ownership

The humanoid robot GPU market remains expensive to scale because compute hardware is one of the most difficult costs to compress without reducing capability. Robots-as-a-Service can spread spending over time, but that does not remove the underlying hardware cost, since the GPU still sits inside the monthly or usage-based fee charged to the customer. The humanoid robot GPU market is, therefore, more accessible today to developers and buyers who can absorb higher upfront engineering costs while waiting for volume efficiencies to emerge. Qualcomm’s CES 2026 robotics platform launch showed why integrated CPU, GPU, and AI acceleration designs are being closely watched, as they offer a path to lower board complexity and better power efficiency in a single package. Until integrated platforms scale more broadly, the cost base is likely to keep commercial deployment concentrated among well-funded robot makers and early industrial adopters.

*Our forecasts treat driver/restraint impacts as directional, not additive. The impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By GPU Type: Data Center Training Spending Anchors the Market While Integrated Platforms Gain Pace

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.[2]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.

By Deployment Type: Offboard Training Leads While Onboard Revenue Builds Faster

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.

By GPU Function: Training Workloads Dominate While Synthetic Data Systems Rise Quickly

Training and Simulation accounted for 48.87% of revenue in 2025, confirming that the humanoid robot GPU market continues to allocate the largest share of spend to model creation and refinement. That position is supported by the broad need to train vision, control, and reasoning models before fleets can handle changing tasks in live settings. NVIDIA reinforced this function by launching open humanoid model frameworks and cloud-to-robot infrastructure intended to keep training, fine-tuning, and simulation on its stack. The same humanoid robot GPU market also depends on inference and perception functions that process visual input, depth data, mapping, and task-state awareness once robots are deployed. Motion planning and control remain a separate compute requirement because stable actuation needs high-frequency execution with less tolerance for delay than many other AI tasks.

Digital Twin and Synthetic Data Generation is projected to grow at a 38.26% CAGR through 2031, which makes it the fastest-growing GPU function. The humanoid robot GPU market is moving in this direction because real-world data collection cannot cover enough edge cases or task variety at commercial speeds. NVIDIA’s synthetic motion pipeline result of 780,000 trajectories in 11 hours, along with peer-reviewed work using 64 NVIDIA L40 GPUs across 1,024 simulated environments per GPU, shows how simulation has become an operating necessity rather than an experimental add-on. In the humanoid robot GPU industry, that shift creates demand for both larger training clusters and faster iteration between simulated and real robot behavior.

By Robot Capability: Perception Holds the Base While Multi-Modal Reasoning Raises Compute Needs

Real-Time Perception held the largest share at 35.63% in 2025, reflecting that the humanoid robot GPU market is still grounded in workloads such as object recognition, scene awareness, and obstacle handling. Industrial deployments remain perception-heavy because those tasks are the first to reach a practical level of reliability in structured environments. BMW’s Leipzig deployment of Hexagon Robotics’ AEON included a 21-sensor stack with cameras, radar, and force-torque sensing, which shows the continuous perception burden that live industrial humanoids place on embedded compute. The humanoid robot GPU market, therefore, continues to treat perception as the baseline capability that every commercial system must support before higher-order autonomy can scale. Motion planning and dexterous manipulation also remain closely linked to this segment because richer sensing enables a robot to perform more fine-motor tasks in real time.

Multi-Modal Reasoning is forecast to grow at a 37.92% CAGR through 2031, making it the fastest-growing capability area. The humanoid robot GPU market is moving in this direction because buyers want robots that can interpret natural language, adapt to new environments, and complete multi-step tasks without repeated manual reprogramming. NVIDIA’s early access GR00T N1.7 update added finger-level dexterous control for contact-rich tasks, which signals a move toward more capable policies that combine perception, reasoning, and manipulation in the same system. As those capabilities mature, the humanoid robot GPU market will need stronger onboard modules with enough memory and bandwidth to support larger real-time models.

By End Use Industry: Automotive Leads Commercial Adoption While Broader Manufacturing Speeds Up

Automotive held the largest end-use share at 32.51% in 2025, which made it the clearest commercial anchor for the humanoid robot GPU market. BMW gave the most detailed production example in the draft, showing how Figure AI robots supported output over a 10-month period while accumulating meaningful operating hours and part-handling volume inside a real plant. The humanoid robot GPU market also benefited from the automotive sector, as the sector can justify higher initial system costs when labor gaps, workflow repetition, and production continuity create a clear path to value. Logistics and warehousing formed the next visible demand layer, supported by Agility Robotics deployments and the December 2025 Mercado Libre agreement that extended commercial humanoid use into fulfillment operations in South America. That mix of factory and logistics use cases matters because it broadens the set of tasks that justify the use of onboard inference hardware.

Manufacturing and Assembly is projected to grow at a 38.49% CAGR through 2031, which makes it the fastest-growing end-use segment in the humanoid robot GPU market. The expected acceleration comes from the transfer of lessons learned in automotive into electronics, semiconductor, and general industrial assembly settings where repetitive handling and inspection work can also benefit from embodied AI. The humanoid robot GPU market should therefore see demand widen from a few flagship automotive programs toward a larger set of industrial buyers that want flexible automation without redesigning every station around a fixed robot cell. Research, healthcare, and defense still represent longer-cycle opportunities, but they are likely to remain secondary until capability, safety, and cost improve further.

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.[3]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.

Competitive Landscape

The humanoid robot GPU market is highly concentrated at the compute platform level, with NVIDIA holding the strongest position through a stack that spans data center training systems, Jetson onboard modules, and the Isaac GR00T development environment. NVIDIA reinforced that role by presenting physical AI as a cloud-to-robot compute model rather than a collection of disconnected products, which helps keep training, simulation, and inference on the same platform family. The humanoid robot GPU market reflects that platform strength in public adoption signals from companies such as Boston Dynamics and 1X, both of which aligned advanced humanoid programs with Jetson Thor. Once developers build around a training and simulation stack, switching becomes harder because software tools, model workflows, and deployment hardware are all connected. That makes platform control as important as raw silicon performance in this market.

Qualcomm is the clearest challenger named in the draft, and its CES 2026 robotics platform launch showed a direct attempt to compete on integrated performance, power efficiency, and developer tooling in the humanoid robot GPU market.[4]Qualcomm framed the offer as a full robotics technology suite for systems ranging from household robots to full-size humanoids, which signaled a serious intent to challenge NVIDIA in embedded compute. NVIDIA answered from the opposite direction by introducing the Isaac GR00T Reference Humanoid Robot in June 2026, which pushed its ecosystem further downstream into a packaged research platform. Boston Dynamics expanded its collaboration with NVIDIA in March 2025, while Agility Robotics said it would adopt Jetson Thor for the sixth-generation Digit platform, showing how leading robot developers are locking compute choices into future product cycles. The humanoid robot GPU market also has pressure from Chinese compute vendors in onboard applications, especially where energy efficiency and localization are prioritized over maximum peak performance.

The next competitive battleground in the humanoid robot GPU market is likely to sit in functional safety, workload isolation, and fleet-scale orchestration rather than in headline compute numbers alone. No company in the draft had yet established a purpose-built safety-certified compute standard for broad humanoid deployment, which leaves room for differentiation as industrial rollouts mature. The humanoid robot GPU market also favors vendors that can capture recurring upgrade demand from Robots-as-a-Service fleets, because those operators replace, refresh, and optimize compute over time instead of making a single purchase. For that reason, vendors with strong software control, integrated reference designs, and deployment partnerships are better positioned than vendors that compete only on chip-level specifications.

Recent Industry Developments

• June 2026: NVIDIA announced the Isaac GR00T Reference Humanoid Robot at GTC Taipei, the first open humanoid robot reference design integrating a Unitree H2 Plus chassis with NVIDIA Jetson AGX Thor T5000 onboard compute delivering 2,070 FP4 teraflops. The platform is designed for academic and commercial research teams and is expected to be available from Unitree in late 2026, lowering the barrier to GPU-accelerated humanoid development across universities and robotics startups globally.
• June 2026: NVIDIA released Isaac GR00T N1.7 in early access with commercial licensing included, enabling production deployments of GR00T-based humanoid robot policies. N1.7 added finger-level dexterous control support for contact-rich tasks such as small parts assembly.
• April 2026: BMW Group launched the full pilot phase of its Leipzig humanoid robot deployment using Hexagon Robotics’ AEON, marking the first humanoid robot in active automotive production in Europe. The deployment follows a December 2025 initial test and builds on the 10-month Spartanburg pilot with Figure AI that contributed to the production of over 30,000 BMW X3 vehicles.
• February 2026: Agility Robotics announced a commercial Robots-as-a-Service agreement with Toyota Motor Manufacturing Canada for 7 Digit humanoid robots at the Woodstock, Ontario RAV4 production facility, establishing the first commercially contracted humanoid deployment in the Canadian automotive industry. Each Digit unit deploys NVIDIA Jetson Thor for onboard inference, directly contributing to the onboard GPU segment’s revenue inflection.

List of Companies Covered in this Report:

• NVIDIA Corporation
• Qualcomm Incorporated
• Intel Corporation
• Advanced Micro Devices, Inc.
• Huawei Technologies Co., Ltd.
• Tesla, Inc.
• Baidu, Inc.
• Horizon Robotics Inc.
• Rockchip Electronics Co., Ltd.
• MediaTek Inc.
• Samsung Electronics Co., Ltd.
• Alphabet Inc.
• Amazon.com, Inc.
• Foxconn Technology Co., Ltd.
• Agility Robotics, Inc.
• Figure AI, Inc.
• Apptronik, Inc.
• Boston Dynamics, Inc.
• UBTECH Robotics Corp Ltd.
• Unitree Robotics
• Sanctuary Cognitive Systems Corporation
• 1X Technologies AS
• Fourier Intelligence Co., Ltd.
• XPENG Inc.
• Siemens AG
• ABB Ltd.
• 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

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