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市场调查报告书
商品编码
1993617

全球视觉语言模型市场：按部署方式、产业、模型类型和地区划分－市场规模、产业趋势、机会分析与未来预测（2026-2035 年）

Global Vision-Language Models Market: By Deployment Mode, Industry Vertical, Model Type, Region - Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 310 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

全球视觉语言模型（VLM）市场预计将迎来显着成长，到2025年将达到约38.4亿美元。未来十年，该市场预计将大幅扩张，到2035年将达到417.5亿美元。这一成长意味着在2026年至2035年的预测期内，复合年增长率（CAGR）约为26.95%。这种快速扩张是由几个关键的技术和市场趋势所驱动的，这些趋势正在改变VLM市场的模式。

推动这一快速成长的主要因素之一是超大规模硬体平台的进步，例如英伟达的 Blackwell GPU 和 Cerebras 的晶圆级引擎 3 (WSE-3)。这些强大的运算基础设施提供了训练和部署日益复杂、规模庞大的视觉语言模型所需的巨大处理能力。除了硬体的改进之外，我们还看到人工智慧模型正朝着可操作的方向发生重大转变，这些模型不仅能够理解视觉和文字数据，还能产生直接影响决策和自动化流程的输出。

显着的市场趋势

全球视觉语言模式（VLM）市场的科技巨头正日益推行垂直整合策略，专注于收购专业影像相关企业，其主要目的在于取得有价值的数据，而非依赖现有的收入来源。这一转变凸显了人们认识到，专有资料集（例如卫星影像提供者和医疗檔案库所持有的资料集）可以构成重要的竞争优势，即「护城河」。

同时，创业投资在虚拟生命週期管理（VLM）领域的投资趋势也在改变。投资人不再像以前那样投资那些需要大量资金、专注于从零开始开发基础模型的「模型建构者」。相反，他们正将资源转向“VLM应用层”，支持那些利用成熟可靠的模型（例如Llama 3.2）来建立针对特定垂直行业工作流程的解决方案的Start-Ups。

Milestone Systems 是这种策略重点的绝佳例证，该公司是资料驱动型影像技术的全球领导者。该公司近期发布了一款基于 NVIDIA Cosmos Reason 构建的高级视觉语言模型 (VLM)，专为理解交通状况而设计。这款专用 VLM 清楚地展示了企业如何利用自身资料和尖端人工智慧框架，部署客製化的视觉语言解决方案，以应对复杂的、特定领域的难题。

关键成长要素

2025年至2026年间，视觉-语言-行为（VLA）架构的推出标誌着视觉-语言模型（VLM）市场取得了突破性的技术进步。这项创新与传统的VLM截然不同，后者主要基于视觉和语言输入来产生文字输出。而VLA则产生控制讯号，从而实现与环境的直接物理交互，例如机器人运动和操作指令。这种转变使VLM从被动的资讯解释器转变为能够在真实环境中执行复杂任务的主动智能体。

新机会的趋势

由于基于代理的人工智慧（尤其是自主视觉代理）的兴起，视觉语言模型（VLM）市场正经历着一场变革。这些先进的人工智慧系统旨在自主运行，在动态环境中解读视觉和文字资料并与之交互，而无需持续的人工干预。这一演变预示着一个新时代的到来：人工智慧代理不再只是被动的工具，而是能够基于视觉理解做出复杂决策并解决问题的积极参与者。

优化障碍

儘管视觉语言模型（VLM）发展迅速，但一种被称为「物体幻觉」的顽固挑战仍然影响着它们的可靠性。这种现像是指模型错误地辨识出视觉输入中实际上不存在的物体，导致误判。虽然技术进步已显着降低了此类错误的发生频率，但目前最先进模型的行业标准错误率仍在3%左右。儘管这比前几代模型有所改进，但在对准确性和精确度要求极高的应用中，这仍然是一个相当大的误差范围。

目录

第一章执行摘要：全球视觉语言模型市场

第二章：报告概述

  • 研究框架
    • 研究目标
    • 市场的定义
    • 市场区隔
  • 调查方法
    • 市场规模估算
    • 定性研究
    • 量化研究
    • 按地区分類的主要调查受访者组成
    • 数据检验
    • 调查的前提

第三章：全球视觉语言模型市场概览

  • 产业价值链分析
    • 数据收集和标註
    • 模型开发和训练（人工智慧实验室/云端服务提供者）
    • 基础设施和部署（云端/硬体）
  • 产业展望
    • 开放原始码视觉语言模型的发展
    • 跨产业多模态人工智慧实施（2025 年）
    • 多模态人工智慧在机器人和现实世界系统中的扩展
  • PESTLE分析
  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争强度
  • 市场成长及前景
    • 市场获利估算与预测（2020-2035）
  • 市场吸引力分析
    • 按型号
  • 可执行的见解（分析师建议）

第四章：竞争对手仪錶板

  • 市场集中度
  • 企业市场占有率分析
  • 竞争对手分析与基准测试

第五章：全球视觉语言模型市场分析

  • 市场动态和趋势
    • 成长要素
    • 抑制因子
    • 机会
    • 主要趋势
  • 市场规模及预测（2020-2035）
    • 透过部署方法
    • 按型号
    • 按行业
    • 按地区

第六章：北美视觉语言模型市场分析

第七章：欧洲视觉语言模型市场分析

第八章：亚太地区视觉语言模式市场分析

第九章：中东和非洲视觉语言模型市场分析

第十章：南美洲视觉语言模型市场分析

第十一章：公司简介（公司概况、历史沿革、组织架构、主要产品线、财务指标、主要客户/产业、主要竞争对手、SWOT 分析、联络方式、业务策略展望）

  • 世界公司
    • Adobe Research
    • Alibaba DAMO Academy
    • Amazon Web Services (AWS)
    • Apple
    • Baidu
    • ByteDance AI Lab
    • Google DeepMind
    • Huawei Cloud AI
    • IBM Research
    • Meta (Facebook AI Research)
    • Microsoft
    • NVIDIA
    • OpenAI
    • Oracle
    • Salesforce Research
    • Samsung Research
    • SAP AI
    • SenseTime
    • Tencent AI Lab
    • TikTok AI Lab
    • 其他主要企业

第十二章附录

简介目录
Product Code: AA02261703

The global Vision-Language Models (VLM) market is poised for remarkable growth, with its valuation reaching approximately USD 3.84 billion in 2025. Over the following decade, this market is expected to expand dramatically, projected to hit an impressive USD 41.75 billion by 2035. This growth corresponds to a compound annual growth rate (CAGR) of about 26.95% during the forecast period from 2026 to 2035. Such rapid expansion is fueled by several key technological and market trends that are reshaping the landscape of VLMs.

One of the primary drivers behind this surge is the advancement of hyperscale hardware platforms, such as NVIDIA's Blackwell GPUs and Cerebras' Wafer-Scale Engine 3 (WSE-3). These powerful computing infrastructures provide the immense processing capabilities required to train and deploy increasingly complex and large-scale vision-language models. Alongside hardware improvements, there is a significant shift toward actionable AI models that not only understand visual and textual data but also generate outputs that can directly influence decision-making and automation processes.

Noteworthy Market Developments

Tech giants in the global Vision-Language Models (VLM) market are increasingly pursuing a strategy of vertical integration, focusing on acquiring specialized imaging companies primarily for their valuable data rather than their existing revenue streams. This shift highlights the recognition that proprietary datasets, such as those held by satellite imagery providers and medical archives, serve as critical competitive advantages or "moats."

Simultaneously, venture capital investment dynamics within the VLM space have evolved, moving away from the heavily capital-intensive "Model Builders" who focus on developing foundational models from scratch. Instead, investors are now channeling their resources into the "VLM Application Layer," backing startups that leverage established, powerful models like Llama 3.2 to create solutions tailored for specific vertical workflows.

An illustrative example of this strategic focus is Milestone Systems, a global leader in data-driven video technology. Recently, the company launched an advanced vision-language model designed specifically for traffic understanding, powered by NVIDIA Cosmos Reason. This specialized VLM exemplifies how companies are deploying tailored vision-language solutions to tackle complex, domain-specific problems, leveraging both proprietary data and cutting-edge AI frameworks.

Core Growth Drivers

The period spanning 2025 to 2026 witnessed a groundbreaking technical advancement in the Vision-Language Models (VLM) market with the introduction of the Vision-Language-Action (VLA) architecture. This innovation represents a significant departure from traditional VLMs, which primarily generate textual outputs based on visual and linguistic inputs. Instead, VLAs produce control signals that enable direct physical interaction with the environment, such as robotic movements or manipulation commands. This shift transforms VLMs from passive interpreters of information into active agents capable of executing complex tasks in real-world settings.

Emerging Opportunity Trends

The Vision-Language Models (VLM) market is currently undergoing a transformative shift driven by the emergence of agentic AI, particularly in the form of autonomous visual agents. These advanced AI systems are designed to operate independently, interpreting and interacting with visual and textual data in dynamic environments without constant human oversight. This evolution marks a new era where AI agents are not merely passive tools but active participants capable of complex decision-making and problem-solving based on their visual understanding.

Barriers to Optimization

Despite the rapid progress made in Vision-Language Models (VLMs), a persistent challenge known as "object hallucination" continues to affect their reliability. This phenomenon occurs when models inaccurately identify or perceive objects that do not actually exist within the visual input, leading to false positives in their interpretations. Although advancements have significantly reduced the frequency of such errors, the current industry standard error rate for leading-edge models remains around 3%. While this marks an improvement compared to earlier generations, it is still a considerable margin of error for applications where precision and accuracy are absolutely critical.

Detailed Market Segmentation

By Model Type, Image-text Vision-Language Models (VLMs) held a commanding lead in the market, capturing a 44.50% share of the total. This dominant position is largely attributable to their exceptional ability to align visual and textual information with high precision. The superior visual-text alignment offered by these models allows them to understand and interpret complex scenes more accurately than other model types, making them highly versatile and effective across a wide range of applications.

By Industry, the IT and Telecom sector emerged as the foremost vertical within the Vision-Language Models (VLM) market, accounting for a 16% share of the total market. This leading position is largely driven by the sector's increasing reliance on advanced AI technologies to enhance network monitoring capabilities. As telecommunications networks grow more complex and data-intensive, the adoption of VLMs has accelerated to address the need for sophisticated tools that can analyze and interpret vast amounts of visual and textual data in real time.

By Deployment, cloud-based solutions overwhelmingly dominated the deployment landscape of the Vision-Language Models (VLM) market, capturing a substantial 66% share of the total revenue. This dominance reflects the growing preference among enterprises for cloud platforms that offer scalable, flexible, and cost-effective AI infrastructure capable of handling the complex computational demands of VLMs. The ability to deploy and run large-scale vision-language models in the cloud enables organizations to quickly access advanced AI capabilities without the need for extensive on-premises hardware investments.

Segment Breakdown

By Vehicle

  • Commercial Vehicle
  • Passenger Car

By Propulsion

  • Bev
  • Hev
  • Phev

By Communication Technology

  • Controller Area Network
  • Local Interconnect Network
  • Flexray, Ethernet

By Function

  • Predictive Technology
  • Autonomous Driving/ADAS (Advanced Driver Assistance System)

By Application

  • Powertrain
  • Breaking System
  • Body Electronics
  • ADAS
  • Infotainment

By Region

  • North America
  • Europe
  • Asia Pacific
  • Middle East and Africa
  • South America

Geography Breakdown

  • In 2025, North America led the Vision-Language Models (VLM) market, securing the largest share of revenue at 45%. This leadership position is not only due to the scale of the models developed in the region but also because of a strategic shift toward more advanced, "reasoning-heavy" architectures such as Gemini 2.5 Pro and GPT-4.1. These sophisticated models go beyond basic image recognition, enabling complex visual reasoning capabilities that are increasingly integrated into enterprise workflows.
  • The growth is also propelled by the dynamic innovation environment in Silicon Valley, where venture capital investment is aggressively targeting the development of Hybrid VLM-LLM Controllers. These cutting-edge technologies serve as interfaces that allow foundational vision-language models to connect directly with proprietary enterprise databases. This capability enhances the practical utility of VLMs by enabling seamless access to and interaction with company-specific data, thereby unlocking new efficiencies and insights for businesses.

Leading Market Participants

  • Adobe Research
  • Alibaba DAMO Academy
  • Amazon Web Services (AWS)
  • Apple
  • Baidu

ByteDance AI Lab

  • Google DeepMind
  • Huawei Cloud AI
  • IBM Research
  • Meta (Facebook AI Research)
  • Microsoft
  • NVIDIA
  • OpenAI
  • Oracle
  • Salesforce Research
  • Samsung Research
  • SAP AI
  • SenseTime
  • Tencent AI Lab
  • TikTok AI Lab
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global Vision-Language Models Market

Chapter 2. Report Description

  • 2.1. Research Framework
    • 2.1.1. Research Objective
    • 2.1.2. Market Definitions
    • 2.1.3. Market Segmentation
  • 2.2. Research Methodology
    • 2.2.1. Market Size Estimation
    • 2.2.2. Qualitative Research
      • 2.2.2.1. Primary & Secondary Sources
    • 2.2.3. Quantitative Research
      • 2.2.3.1. Primary & Secondary Sources
    • 2.2.4. Breakdown of Primary Research Respondents, By Region
    • 2.2.5. Data Triangulation
    • 2.2.6. Assumption for Study

Chapter 3. Global Vision-Language Models Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. Data Collection & Annotation
    • 3.1.2. Model Development & Training (AI Labs / Cloud Providers)
    • 3.1.3. Infrastructure & Deployment (Cloud / Hardware)
  • 3.2. Industry Outlook
    • 3.2.1. Growth in Open-Source Vision-Language Models
    • 3.2.2. Adoption of Multimodal AI Across Industries (2025)
    • 3.2.3. Expansion of Multimodal AI in Robotics & Real-World Systems
  • 3.3. PESTLE Analysis
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Bargaining Power of Suppliers
    • 3.4.2. Bargaining Power of Buyers
    • 3.4.3. Threat of Substitutes
    • 3.4.4. Threat of New Entrants
    • 3.4.5. Degree of Competition
  • 3.5. Market Growth and Outlook
    • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
  • 3.6. Market Attractiveness Analysis
    • 3.6.1. By Model Type
  • 3.7. Actionable Insights (Analyst's Recommendations)

Chapter 4. Competition Dashboard

  • 4.1. Market Concentration Rate
  • 4.2. Company Market Share Analysis (Value %), 2025
  • 4.3. Competitor Mapping & Benchmarking

Chapter 5. Global Vision-Language Models Market Analysis

  • 5.1. Market Dynamics and Trends
    • 5.1.1. Growth Drivers
      • 5.1.1.1. Rising Demand for Multimodal AI to Enable Human-Like Understanding and Automation
    • 5.1.2. Restraints
    • 5.1.3. Opportunity
    • 5.1.4. Key Trends
  • 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 5.2.1. By Deployment Mode
      • 5.2.1.1. Key Insights
        • 5.2.1.1.1. Cloud-based
        • 5.2.1.1.2. On premises
        • 5.2.1.1.3. Hybrid
    • 5.2.2. By Model Type
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Image-Text Vision-Language Models
          • 5.2.2.1.1.1. Image captioning models
          • 5.2.2.1.1.2. Visual question answering
        • 5.2.2.1.2. Video-Text Vision-Language Models
          • 5.2.2.1.2.1. Video understanding
          • 5.2.2.1.2.2. Video summarization
        • 5.2.2.1.3. Document Vision-Language Models (DocVLMs)
          • 5.2.2.1.3.1. OCR + reasoning
          • 5.2.2.1.3.2. Layout understanding
        • 5.2.2.1.4. Other Multimodal VLM Types
    • 5.2.3. By Industry Vertical
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. IT & Telecom
        • 5.2.3.1.2. BFSI
        • 5.2.3.1.3. Retail & E-commerce
        • 5.2.3.1.4. Healthcare & Life Sciences
        • 5.2.3.1.5. Media & Entertainment
        • 5.2.3.1.6. Manufacturing
        • 5.2.3.1.7. Automotive & Mobility
        • 5.2.3.1.8. Government & Defense
        • 5.2.3.1.9. Other Industries
    • 5.2.4. By Region
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. North America
          • 5.2.4.1.1.1. The U.S.
          • 5.2.4.1.1.2. Canada
          • 5.2.4.1.1.3. Mexico
        • 5.2.4.1.2. Europe
          • 5.2.4.1.2.1. Western Europe
            • 5.2.4.1.2.1.1. The UK
            • 5.2.4.1.2.1.2. Germany
            • 5.2.4.1.2.1.3. France
            • 5.2.4.1.2.1.4. Italy
            • 5.2.4.1.2.1.5. Spain
            • 5.2.4.1.2.1.6. Rest of Western Europe
          • 5.2.4.1.2.2. Eastern Europe
            • 5.2.4.1.2.2.1. Poland
            • 5.2.4.1.2.2.2. Russia
            • 5.2.4.1.2.2.3. Rest of Eastern Europe
        • 5.2.4.1.3. Asia Pacific
          • 5.2.4.1.3.1. China
          • 5.2.4.1.3.2. India
          • 5.2.4.1.3.3. Japan
          • 5.2.4.1.3.4. South Korea
          • 5.2.4.1.3.5. Australia & New Zealand
          • 5.2.4.1.3.6. ASEAN
          • 5.2.4.1.3.7. Rest of Asia Pacific
        • 5.2.4.1.4. Middle East & Africa
          • 5.2.4.1.4.1. UAE
          • 5.2.4.1.4.2. Saudi Arabia
          • 5.2.4.1.4.3. South Africa
          • 5.2.4.1.4.4. Rest of MEA
        • 5.2.4.1.5. South America
          • 5.2.4.1.5.1. Argentina
          • 5.2.4.1.5.2. Brazil
          • 5.2.4.1.5.3. Rest of South America

Chapter 6. North America Vision-Language Models Market Analysis

  • 6.1. Market Dynamics and Trends
    • 6.1.1. Growth Drivers
    • 6.1.2. Restraints
    • 6.1.3. Opportunity
    • 6.1.4. Key Trends
  • 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 6.2.1. By Deployment Mode
    • 6.2.2. By Model Type
    • 6.2.3. By Industry Vertical
    • 6.2.4. By Country

Chapter 7. Europe Vision-Language Models Market Analysis

  • 7.1. Market Dynamics and Trends
    • 7.1.1. Growth Drivers
    • 7.1.2. Restraints
    • 7.1.3. Opportunity
    • 7.1.4. Key Trends
  • 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 7.2.1. By Type
    • 7.2.2. By Deployment Mode
    • 7.2.3. By Model Type
    • 7.2.4. By Industry Vertical
    • 7.2.5. By Country

Chapter 8. Asia Pacific Vision-Language Models Market Analysis

  • 8.1. Market Dynamics and Trends
    • 8.1.1. Growth Drivers
    • 8.1.2. Restraints
    • 8.1.3. Opportunity
    • 8.1.4. Key Trends
  • 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 8.2.1. By Deployment Mode
    • 8.2.2. By Model Type
    • 8.2.3. By Industry Vertical
    • 8.2.4. By Country

Chapter 9. Middle East & Africa Vision-Language Models Market Analysis

  • 9.1. Market Dynamics and Trends
    • 9.1.1. Growth Drivers
    • 9.1.2. Restraints
    • 9.1.3. Opportunity
    • 9.1.4. Key Trends
  • 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 9.2.1. By Deployment Mode
    • 9.2.2. By Model Type
    • 9.2.3. By Industry Vertical
    • 9.2.4. By Country

Chapter 10. South America Vision-Language Models Market Analysis

  • 10.1. Market Dynamics and Trends
    • 10.1.1. Growth Drivers
    • 10.1.2. Restraints
    • 10.1.3. Opportunity
    • 10.1.4. Key Trends
  • 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 10.2.1. By Deployment Mode
    • 10.2.2. By Model Type
    • 10.2.3. By Industry Vertical
    • 10.2.4. By Country

Chapter 11. Company Profile (Company Overview, Company Timeline, Organization Structure, Key Product landscape, Financial Matrix, Key Customers/Sectors, Key Competitors, SWOT Analysis, Contact Address, and Business Strategy Outlook)

  • 11.1. Global Players
    • 11.1.1. Adobe Research
    • 11.1.2. Alibaba DAMO Academy
    • 11.1.3. Amazon Web Services (AWS)
    • 11.1.4. Apple
    • 11.1.5. Baidu
    • 11.1.6. ByteDance AI Lab
    • 11.1.7. Google DeepMind
    • 11.1.8. Huawei Cloud AI
    • 11.1.9. IBM Research
    • 11.1.10. Meta (Facebook AI Research)
    • 11.1.11. Microsoft
    • 11.1.12. NVIDIA
    • 11.1.13. OpenAI
    • 11.1.14. Oracle
    • 11.1.15. Salesforce Research
    • 11.1.16. Samsung Research
    • 11.1.17. SAP AI
    • 11.1.18. SenseTime
    • 11.1.19. Tencent AI Lab
    • 11.1.20. TikTok AI Lab
    • 11.1.21. Other Prominent Players

Chapter 12. Annexure

  • 13.1 List of Secondary Sources
  • 13.2 Key Country Markets- Macro Economic Outlook/Indicators