边缘人工智慧硬体市场－全球产业规模、份额、趋势、机会及预测（依设备、功耗、功能、处理器、垂直产业、区域和竞争格局划分），2021-2031年

全球边缘人工智慧硬体市场预计将从2025年的261.1亿美元大幅成长至2031年的688.5亿美元，复合年增长率（CAGR）达17.54%。此细分市场涵盖专用实体元件，具体包括神经处理单元（NPU）、图形处理单元（GPU）和专用积体电路（ASIC），这些元件旨在本地处理机器学习演算法，而无需依赖集中式云端连接。推动该市场成长的根本因素是决策流程中对超低延迟的迫切需求，以及透过减少资料传输需求来优化频宽利用率的驱动。此外，严格的资料隐私法规的实施和物联网（IoT）设备的指数级增长也发挥了重要的催化作用，从而催生了对强大的设备端处理能力的迫切需求。

然而，在能源效率方面，它们面临着巨大的挑战，因为将高效能运算整合到资源受限的电池供电设备中是一项艰鉅的技术难题。硬体需求的激增反映了整个晶片行业的趋势。根据半导体产业协会（SIA）预测，到2024年，全球半导体销售额将达到6,276亿美元，这数字主要受汽车和工业领域对人工智慧能力的爆炸性需求所驱动。对底层硅晶片的如此大规模的资本投入，凸显了整个产业向智慧分散式硬体架构转型的趋势。

市场驱动因素

物联网和智慧互联设备的快速扩张正成为边缘人工智慧硬体市场的关键加速器，有效地将处理工作负载从集中式云端基础设施转移到本地环境。随着数十亿感测器和终端部署在工业应用中，传输原始资料相关的延迟和频宽成本已达到难以承受的程度，因此亟需采用片上处理解决方案。这种分散式策略能够实现即时数据过滤和分析，这对于从智慧城市基础设施到工业监控系统等各种应用至关重要。互联终端数量的庞大也凸显了这个趋势的规模。根据爱立信于2024年6月发布的《行动报告》，预计到2025年底，蜂巢式物联网连接总数将达到约45亿，这迫切需要能够在网路边缘实现低功耗、高效能推理的硬体。

同时，人工智慧在自动驾驶汽车和机器人领域的日益融合，正推动硬体朝着兼具高性能和高能效的推理引擎方向发展。这些自主系统依靠先进的神经网路在非结构化环境中安全导航，从而带动了对专用神经网路处理器（NPU）和图形处理器（GPU）的需求，这些处理器无需依赖网路即可执行复杂的逻辑运算。根据国际机器人联合会（IFR）于2024年9月发布的《2024年世界运作报告》，2023年全球工业机器人数量将达到创纪录的428万台，显示智慧自动化的基础正在不断深化。为了满足这些应用所需的计算强度，记忆体频宽和处理速度都至关重要。事实上，世界半导体贸易统计（WSTS）2024年12月的预测显示，2024年记忆体积体电路市场将成长81.0%，这凸显了基础设施调整以支援高阶人工智慧工作负载的必要性。

市场挑战

能源效率仍是全球边缘人工智慧硬体市场成长的一大障碍。製造商在寻求将先进的机器学习功能嵌入小型设备时，面临着一个固有的权衡：既要提供高运算效能，也要保持低功耗。边缘设备，尤其是那些用于远端工业设施和可穿戴技术的设备，通常依赖有限的电池供电。即时人工智慧推理所需的繁重处理会迅速消耗这些电量，从而缩短硬体的运作并降低其可靠性。这种技术限制阻碍了潜在买家在关键任务应用中部署智慧边缘解决方案，因为这些应用需要持续运作，从而影响了边缘解决方案的商业性化应用。

这场电力挑战的严峻性从待升级设备生态系统的庞大规模可见一斑。根据GSMA预测，到2024年，企业级物联网连接数将达到107亿，这将建构一个庞大的基础设施，而高效运作需要节能的处理能力。除非开发出既能提供高效能又能严格控制功耗的硬件，否则如此庞大的连网设备将无法充分利用分散式人工智慧，从而直接限制市场的成长潜力。

市场趋势

将专用神经处理单元 (NPU) 整合到行动系统晶片 (SoC) 中，正透过实现设备内复杂推理，为生成式人工智慧 (AI) 应用带来革命性的变革。製造商正越来越多地将高效加速器直接嵌入智慧型手机处理器中，以本地处理即时语言翻译和影像处理等任务，从而显着降低延迟并减少对云端服务的依赖。这种架构转变正在推动商业性的重大升级，消费者对人工智慧旗舰设备的强劲需求便是最好的证明。正如三星电子在 2025 年 1 月发布的《2024 财年第四季及全年财务报告》中所述，该公司销售业绩强劲，其搭载 Galaxy AI 的旗舰 Galaxy S24 系列实现了两位数增长，凸显了市场向硬体赋能智慧的快速转型。

同时，晶片组技术和异质整合技术的应用正在重新定义半导体设计，以克服边缘硬体单晶粒在物理和经济上的规模限制。透过将采用不同製程节点製造的小型模组化晶粒整合到单一封装中，工程师可以提高产量比率，同时针对特定的人工智慧工作负载优化效能和成本。这种製造技术的演进对于满足用于高效能运算的下一代边缘处理器的频宽和互连需求至关重要。根据台积电在2025年1月举行的“2024年第四季财报电话会议”，该公司预测，在高效能运算解决方案的持续需求驱动下，到2025年，支持这些异质架构的先进封装技术带来的收入将超过其总收入的10%。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章 全球边缘人工智慧硬体市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 依装置分类（智慧型手机、机器人、监视录影机、穿戴式装置、智慧音箱、汽车、智慧镜子等）
  • 依功耗（小于1W、1-3W、3-5W、3-5W、大于10W）
  • 按功能（训练、推理）
  • 按处理器类型（CPU、GPU、ASIC、其他）
  • 按产业划分（医疗保健与生命科学、零售与家用电子电器、汽车与运输、航太与国防、政府、建筑、其他）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美边缘人工智慧硬体市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 美国
  • 加拿大
  • 墨西哥

第七章：欧洲边缘人工智慧硬体市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章：亚太边缘人工智慧硬体市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章：中东和非洲边缘人工智慧硬体市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美边缘人工智慧硬体市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章：全球边缘人工智慧硬体市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• Qualcomm Technologies, Inc.
• Huawei Technologies Co. Ltd.
• Samsung Electronics Co. Ltd.
• MediaTek Inc.
• International Business Machines Corporation
• Microsoft Corporation
• NVIDIA Corporation
• Google LLC
• Apple Inc.
• Intel Corporation

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Edge AI Hardware Market is projected to expand significantly, rising from a valuation of USD 26.11 Billion in 2025 to USD 68.85 Billion by 2031, reflecting a compound annual growth rate (CAGR) of 17.54%. This sector encompasses specialized physical components-specifically neural processing units (NPUs), graphics processing units (GPUs), and application-specific integrated circuits (ASICs)-engineered to process machine learning algorithms locally rather than depending on centralized cloud connectivity. The fundamental momentum behind this market stems from the urgent necessity for ultra-low latency in real-time decision-making processes and the drive to optimize bandwidth usage by reducing data transmission requirements. Additionally, the enforcement of strict data privacy regulations and the exponential increase in Internet of Things (IoT) devices act as primary catalysts, creating a distinct need for robust, on-device processing capabilities.

However, the market faces a substantial hurdle regarding power efficiency, as incorporating high-performance computing into resource-constrained, battery-powered devices presents significant technical difficulties. This surge in hardware demand mirrors trends in the wider chip industry; according to the Semiconductor Industry Association, global semiconductor sales hit $627.6 billion in 2024, a figure largely propelled by the explosive demand for artificial intelligence capabilities within automotive and industrial sectors. Such massive capital investment in foundational silicon underscores the industrial-scale transition toward intelligent, decentralized hardware architectures.

Market Driver

The rapid expansion of IoT and smart connected devices serves as a major accelerator for the Edge AI Hardware market, effectively migrating processing workloads from centralized cloud infrastructures to local environments. As industries implement billions of sensors and endpoints, the costs related to latency and bandwidth for transmitting raw data become unmanageable, thereby mandating on-chip processing solutions. This decentralized strategy enables immediate data filtering and analysis, a capability essential for diverse applications from smart city infrastructure to industrial monitoring systems. The scale of this trend is highlighted by the sheer volume of connected endpoints; the "Ericsson Mobility Report" from June 2024 estimates that total cellular IoT connections will reach roughly 4.5 billion by the end of 2025, creating an urgent need for hardware that delivers low-power, high-performance inference at the network edge.

Concurrently, the increasing incorporation of AI into autonomous vehicles and robotics is compelling a hardware evolution toward inference engines that balance high performance with energy efficiency. These autonomous systems depend on advanced neural networks to safely traverse unstructured environments, fueling the demand for specialized NPUs and GPUs capable of complex logic execution without network reliance. According to the International Federation of Robotics (IFR) "World Robotics 2024" report released in September 2024, the global operational stock of industrial robots hit a record 4.28 million units in 2023, signaling a deepening base for intelligent automation. To sustain the computational intensity these applications require, memory bandwidth has become as vital as processing speed; in fact, the World Semiconductor Trade Statistics (WSTS) December 2024 forecast projected the memory integrated circuit segment would jump by 81.0% in 2024, emphasizing the infrastructure adjustments necessary to support advanced AI workloads.

Market Challenge

The issue of power efficiency remains a formidable barrier restricting the growth of the Global Edge AI Hardware Market. As manufacturers attempt to embed sophisticated machine learning features into compact devices, they encounter an inherent conflict between achieving high computational performance and maintaining low energy consumption. Edge devices, especially those utilized in remote industrial locations or wearable technology, often depend on limited battery power. The intensive processing needed for real-time AI inference rapidly depletes these energy reserves, thereby diminishing the hardware's operational lifespan and reliability. This technical limitation causes hesitation among potential buyers regarding the adoption of intelligent edge solutions for mission-critical operations where uninterrupted uptime is essential, consequently stalling widespread commercial acceptance.

The severity of this power challenge is highlighted by the massive scale of the device ecosystem awaiting upgrades. According to the GSMA, the enterprise segment accounted for 10.7 billion IoT connections in 2024, representing a vast infrastructure that necessitates energy-efficient processing to operate effectively. Unless hardware is developed that can provide high-level performance while rigorously managing power consumption, this enormous volume of connected devices will be unable to fully utilize decentralized AI, directly limiting the market's total addressable growth potential.

Market Trends

The integration of dedicated Neural Processing Units (NPUs) into Mobile SoCs is revolutionizing consumer electronics by facilitating complex on-device inference for generative AI applications. Manufacturers are increasingly embedding high-efficiency accelerators directly within smartphone processors to manage tasks such as real-time language translation and image manipulation locally, which significantly reduces latency and reliance on cloud services. This architectural transition is fueling substantial commercial upgrades, illustrated by strong consumer demand for AI-enabled flagship devices. As noted in Samsung Electronics' "Fourth Quarter and FY 2024 Results" report from January 2025, the company observed robust sales performance, with the flagship Galaxy S24 series featuring Galaxy AI achieving double-digit growth, highlighting the market's rapid shift toward hardware-enabled intelligence.

Simultaneously, the adoption of Chiplet Technology and Heterogeneous Integration is redefining semiconductor design to surpass the physical and economic scaling limitations associated with monolithic dies in edge hardware. By amalgamating smaller, modular dies manufactured on distinct process nodes into a single package, engineers can fine-tune performance and costs for specific AI workloads while enhancing yield rates. This evolution in manufacturing is essential for meeting the bandwidth and interconnect demands of next-generation edge processors utilized in high-performance computing. According to the TSMC "Fourth Quarter 2024 Earnings Conference" in January 2025, the company projected that revenue from advanced packaging technologies-which support these heterogeneous architectures-would surpass 10% of its total revenue in 2025, driven by sustained demand for high-performance computing solutions.

Key Market Players

• Qualcomm Technologies, Inc.
• Huawei Technologies Co. Ltd.
• Samsung Electronics Co. Ltd.
• MediaTek Inc.
• International Business Machines Corporation
• Microsoft Corporation
• NVIDIA Corporation
• Google LLC
• Apple Inc.
• Intel Corporation

Report Scope

In this report, the Global Edge AI Hardware Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Edge AI Hardware Market, By Device

• Smartphones
• Robots
• Surveillance Cameras
• Wearables
• Smart Speakers
• Automotive
• Smart Mirrors
• Others

Edge AI Hardware Market, By Power Consumption

• Less Than 1W
• 1-3W
• 3-5W
• 3-5W
• More Than 10W

Edge AI Hardware Market, By Function

• Training
• Inference

Edge AI Hardware Market, By Processor

• CPU
• GPU
• ASIC
• Others

Edge AI Hardware Market, By Vertical

• Healthcare & Life Science
• Retail & Consumer Electronics
• Automotive & Transportation
• Aerospace & Defense
• Government
• Construction
• Others

Edge AI Hardware Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Edge AI Hardware Market.

Available Customizations:

Global Edge AI Hardware Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Edge AI Hardware Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Device (Smartphones, Robots, Surveillance Cameras, Wearables, Smart Speakers, Automotive, Smart Mirrors, Others)
  • 5.2.2. By Power Consumption (Less Than 1W, 1-3W, 3-5W, 3-5W, More Than 10W)
  • 5.2.3. By Function (Training, Inference)
  • 5.2.4. By Processor (CPU, GPU, ASIC, Others)
  • 5.2.5. By Vertical (Healthcare & Life Science, Retail & Consumer Electronics, Automotive & Transportation, Aerospace & Defense, Government, Construction, Others)
  • 5.2.6. By Region
  • 5.2.7. By Company (2025)
• 5.3. Market Map

6. North America Edge AI Hardware Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Device
  • 6.2.2. By Power Consumption
  • 6.2.3. By Function
  • 6.2.4. By Processor
  • 6.2.5. By Vertical
  • 6.2.6. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Edge AI Hardware Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Device
      • 6.3.1.2.2. By Power Consumption
      • 6.3.1.2.3. By Function
      • 6.3.1.2.4. By Processor
      • 6.3.1.2.5. By Vertical
  • 6.3.2. Canada Edge AI Hardware Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Device
      • 6.3.2.2.2. By Power Consumption
      • 6.3.2.2.3. By Function
      • 6.3.2.2.4. By Processor
      • 6.3.2.2.5. By Vertical
  • 6.3.3. Mexico Edge AI Hardware Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Device
      • 6.3.3.2.2. By Power Consumption
      • 6.3.3.2.3. By Function
      • 6.3.3.2.4. By Processor
      • 6.3.3.2.5. By Vertical

7. Europe Edge AI Hardware Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Device
  • 7.2.2. By Power Consumption
  • 7.2.3. By Function
  • 7.2.4. By Processor
  • 7.2.5. By Vertical
  • 7.2.6. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Edge AI Hardware Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Device
      • 7.3.1.2.2. By Power Consumption
      • 7.3.1.2.3. By Function
      • 7.3.1.2.4. By Processor
      • 7.3.1.2.5. By Vertical
  • 7.3.2. France Edge AI Hardware Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Device
      • 7.3.2.2.2. By Power Consumption
      • 7.3.2.2.3. By Function
      • 7.3.2.2.4. By Processor
      • 7.3.2.2.5. By Vertical
  • 7.3.3. United Kingdom Edge AI Hardware Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Device
      • 7.3.3.2.2. By Power Consumption
      • 7.3.3.2.3. By Function
      • 7.3.3.2.4. By Processor
      • 7.3.3.2.5. By Vertical
  • 7.3.4. Italy Edge AI Hardware Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Device
      • 7.3.4.2.2. By Power Consumption
      • 7.3.4.2.3. By Function
      • 7.3.4.2.4. By Processor
      • 7.3.4.2.5. By Vertical
  • 7.3.5. Spain Edge AI Hardware Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Device
      • 7.3.5.2.2. By Power Consumption
      • 7.3.5.2.3. By Function
      • 7.3.5.2.4. By Processor
      • 7.3.5.2.5. By Vertical

8. Asia Pacific Edge AI Hardware Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Device
  • 8.2.2. By Power Consumption
  • 8.2.3. By Function
  • 8.2.4. By Processor
  • 8.2.5. By Vertical
  • 8.2.6. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Edge AI Hardware Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Device
      • 8.3.1.2.2. By Power Consumption
      • 8.3.1.2.3. By Function
      • 8.3.1.2.4. By Processor
      • 8.3.1.2.5. By Vertical
  • 8.3.2. India Edge AI Hardware Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Device
      • 8.3.2.2.2. By Power Consumption
      • 8.3.2.2.3. By Function
      • 8.3.2.2.4. By Processor
      • 8.3.2.2.5. By Vertical
  • 8.3.3. Japan Edge AI Hardware Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Device
      • 8.3.3.2.2. By Power Consumption
      • 8.3.3.2.3. By Function
      • 8.3.3.2.4. By Processor
      • 8.3.3.2.5. By Vertical
  • 8.3.4. South Korea Edge AI Hardware Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Device
      • 8.3.4.2.2. By Power Consumption
      • 8.3.4.2.3. By Function
      • 8.3.4.2.4. By Processor
      • 8.3.4.2.5. By Vertical
  • 8.3.5. Australia Edge AI Hardware Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Device
      • 8.3.5.2.2. By Power Consumption
      • 8.3.5.2.3. By Function
      • 8.3.5.2.4. By Processor
      • 8.3.5.2.5. By Vertical

9. Middle East & Africa Edge AI Hardware Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Device
  • 9.2.2. By Power Consumption
  • 9.2.3. By Function
  • 9.2.4. By Processor
  • 9.2.5. By Vertical
  • 9.2.6. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Edge AI Hardware Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Device
      • 9.3.1.2.2. By Power Consumption
      • 9.3.1.2.3. By Function
      • 9.3.1.2.4. By Processor
      • 9.3.1.2.5. By Vertical
  • 9.3.2. UAE Edge AI Hardware Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Device
      • 9.3.2.2.2. By Power Consumption
      • 9.3.2.2.3. By Function
      • 9.3.2.2.4. By Processor
      • 9.3.2.2.5. By Vertical
  • 9.3.3. South Africa Edge AI Hardware Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Device
      • 9.3.3.2.2. By Power Consumption
      • 9.3.3.2.3. By Function
      • 9.3.3.2.4. By Processor
      • 9.3.3.2.5. By Vertical

10. South America Edge AI Hardware Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Device
  • 10.2.2. By Power Consumption
  • 10.2.3. By Function
  • 10.2.4. By Processor
  • 10.2.5. By Vertical
  • 10.2.6. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Edge AI Hardware Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Device
      • 10.3.1.2.2. By Power Consumption
      • 10.3.1.2.3. By Function
      • 10.3.1.2.4. By Processor
      • 10.3.1.2.5. By Vertical
  • 10.3.2. Colombia Edge AI Hardware Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Device
      • 10.3.2.2.2. By Power Consumption
      • 10.3.2.2.3. By Function
      • 10.3.2.2.4. By Processor
      • 10.3.2.2.5. By Vertical
  • 10.3.3. Argentina Edge AI Hardware Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Device
      • 10.3.3.2.2. By Power Consumption
      • 10.3.3.2.3. By Function
      • 10.3.3.2.4. By Processor
      • 10.3.3.2.5. By Vertical

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Edge AI Hardware Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. Qualcomm Technologies, Inc.
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Huawei Technologies Co. Ltd.
• 15.3. Samsung Electronics Co. Ltd.
• 15.4. MediaTek Inc.
• 15.5. International Business Machines Corporation
• 15.6. Microsoft Corporation
• 15.7. NVIDIA Corporation
• 15.8. Google LLC
• 15.9. Apple Inc.
• 15.10. Intel Corporation

16. Strategic Recommendations

17. About Us & Disclaimer