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市場調查報告書
商品編碼
1993617

全球視覺語言模型市場：按部署方式、產業、模型類型和地區分類－市場規模、產業趨勢、機會分析與未來預測（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個工作天內

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簡介目錄

全球視覺語言模型（VLM）市場預計將迎來顯著成長，到2025年將達到約38.4億美元。未來十年，該市場預計將大幅擴張，到2035年將達到417.5億美元。這一成長意味著在2026年至2035年的預測期內，複合年成長率（CAGR）約為26.95%。這種快速擴張是由幾個關鍵的技術和市場趨勢所驅動的，這些趨勢正在改變VLM市場的模式。

推動這一快速成長的主要因素之一是超大規模硬體平台的進步，例如英偉達的 Blackwell GPU 和 Cerebras 的晶圓級引擎 3 (WSE-3)。這些強大的運算基礎設施提供了訓練和部署日益複雜、規模龐大的視覺語言模型所需的巨大處理能力。除了硬體的改進之外，我們還看到人工智慧模型正朝著可操作的方向發生重大轉變，這些模型不僅能夠理解視覺和文字數據，還能產生直接影響決策和自動化流程的輸出。

顯著的市場趨勢

全球視覺語言模式（VLM）市場的科技巨頭正日益推行垂直整合策略，專注於收購專業影像相關企業，其主要目的在於取得有價值的數據，而非依賴現有的收入來源。這一轉變凸顯了人們認知到，專有資料集（例如衛星影像提供者和醫療檔案庫所持有的資料集）可以構成重要的競爭優勢，即「護城河」。

同時，創業投資在虛擬生命週期管理（VLM）領域的投資趨勢也在改變。投資人不再像以前那樣投資那些需要大量資金、專注於從零開始開發基礎模型的「模型建構者」。相反，他們正將資源轉向“VLM應用層”，支持那些利用成熟可靠的模型（例如Llama 3.2）來建立針對特定垂直行業工作流程的解決方案的Start-Ups。

Milestone Systems 是這種策略重點的絕佳例證，該公司是資料驅動型影像技術的全球領導者。該公司近期發布了一款基於 NVIDIA Cosmos Reason 構建的高級視覺語言模型 (VLM)，專為理解交通狀況而設計。這款專用 VLM 清楚地展示了企業如何利用自身資料和尖端人工智慧框架，部署客製化的視覺語言解決方案，以應對複雜的、特定領域的難題。

關鍵成長要素

2025年至2026年間，視覺-語言-行為（VLA）架構的推出標誌著視覺-語言模型（VLM）市場取得了突破性的技術進步。這項創新與傳統的VLM截然不同，後者主要基於視覺和語言輸入來產生文字輸出。而VLA則產生控制訊號，從而實現與環境的直接物理交互，例如機器人運動和操作指令。這種轉變使VLM從被動的資訊解釋器轉變為能夠在真實環境中執行複雜任務的主動智慧體。

新機會的趨勢

由於基於代理的人工智慧（尤其是自主視覺代理）的興起，視覺語言模型（VLM）市場正經歷著一場變革。這些先進的人工智慧系統旨在自主運行，在動態環境中解讀視覺和文字資料並與之交互，而無需持續的人工干預。這一演變預示著一個新時代的到來：人工智慧代理不再只是被動的工具，而是能夠基於視覺理解做出複雜決策並解決問題的積極參與者。

最佳化障礙

儘管視覺語言模型（VLM）發展迅速，但一種被稱為「物體幻覺」的頑固挑戰仍然影響著它們的可靠性。這種現像是指模型錯誤地辨識出視覺輸入中實際上不存在的物體，導致誤判。雖然技術進步已顯著降低了此類錯誤的發生頻率，但目前最先進模型的行業標準錯誤率仍在3%左右。儘管這比前幾代模型有所改進，但在對準確性和精確度要求極高的應用中，這仍然是一個相當大的誤差範圍。

目錄

第1章摘要整理：全球視覺語言模型市場

第2章：報告概述

  • 研究框架
    • 研究目標
    • 市場的定義
    • 市場區隔
  • 調查方法
    • 市場規模估算
    • 定性研究
    • 量化研究
    • 按地區分類的主要調查受訪者組成
    • 數據檢驗
    • 調查的前提

第3章：全球視覺語言模型市場概覽

  • 產業價值鏈分析
    • 數據收集和標註
    • 模型開發和訓練（人工智慧實驗室/雲端服務提供者）
    • 基礎設施和部署（雲端/硬體）
  • 產業展望
    • 開放原始碼視覺語言模型的發展
    • 跨產業多模態人工智慧實施（2025 年）
    • 多模態人工智慧在機器人和現實世界系統中的擴展
  • PESTLE分析
  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭強度
  • 市場成長及前景
    • 市場獲利估算與預測（2020-2035）
  • 市場吸引力分析
    • 按型號
  • 可執行的見解（分析師建議）

第4章：競爭對手儀錶板

  • 市場集中度
  • 企業市場占有率分析
  • 競爭對手分析與基準測試

第5章：全球視覺語言模型市場分析

  • 市場動態和趨勢
    • 成長要素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測（2020-2035）
    • 透過部署方法
    • 按型號
    • 按行業
    • 按地區

第6章：北美視覺語言模型市場分析

第7章：歐洲視覺語言模型市場分析

第8章：亞太地區視覺語言模式市場分析

第9章：中東和非洲視覺語言模型市場分析

第10章：南美洲視覺語言模型市場分析

第11章：公司簡介（公司概況、歷史沿革、組織架構、主要產品線、財務指標、主要客戶/產業、主要競爭對手、SWOT 分析、聯絡方式、業務策略展望）

  • 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
    • 其他主要企業

第12章附錄

簡介目錄
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