1 INTRODUCTION 56

• 1.1 STUDY OBJECTIVES 56

• 1.2 MARKET DEFINITION 56

  • 1.2.1 INCLUSIONS AND EXCLUSIONS 57

• 1.3 MARKET SCOPE 58

  • 1.3.1 MARKET SEGMENTATION 58

  • 1.3.2 REGIONS COVERED 59

  • 1.3.3 YEARS CONSIDERED 59

• 1.4 CURRENCY CONSIDERED 60

• 1.5 STAKEHOLDERS 60

  • 1.5.1 IMPACT OF RECESSION 60

2 RESEARCH METHODOLOGY 61

• 2.1 RESEARCH DATA 61

  • 2.1.1 SECONDARY DATA 62

  • 2.1.2 PRIMARY DATA 62

    • 2.1.2.1 Breakup of primary profiles 63
    • 2.1.2.2 Key industry insights 63

• 2.2 MARKET SIZE ESTIMATION 64

  • 2.2.1 TOP-DOWN APPROACH 64

  • 2.2.2 BOTTOM-UP APPROACH 64

• 2.3 DATA TRIANGULATION 68

• 2.4 MARKET FORECAST 68

• 2.5 RESEARCH ASSUMPTIONS 69

• 2.6 STUDY LIMITATIONS 71

• 2.7 IMPLICATIONS OF RECESSION ON LARGE LANGUAGE MODEL MARKET 71

3 EXECUTIVE SUMMARY 73

4 PREMIUM INSIGHTS 81

• 4.1 ATTRACTIVE OPPORTUNITIES FOR COMPANIES IN LARGE LANGUAGE MODEL MARKET 81

• 4.2 LARGE LANGUAGE MODEL MARKET: TOP THREE APPLICATIONS 82

• 4.3 NORTH AMERICA: LARGE LANGUAGE MODEL MARKET, BY OFFERING AND END USER 82

• 4.4 LARGE LANGUAGE MODEL MARKET, BY REGION 83

5 MARKET OVERVIEW AND INDUSTRY TRENDS 84

• 5.1 INTRODUCTION 84

• 5.2 MARKET DYNAMICS 84

  • 5.2.1 DRIVERS 85

    • 5.2.1.1 Growth in availability of large datasets 85
    • 5.2.1.2 Advancements in deep learning algorithms 85
    • 5.2.1.3 Need for enhanced human-machine communication 86
    • 5.2.1.4 Rise in demand for automated content creation and curation 87

  • 5.2.2 RESTRAINTS 88

    • 5.2.2.1 High cost of model training & inference optimization 88
    • 5.2.2.2 Data biases and quality concerns 89
    • 5.2.2.3 Lack of transparency in explainability and interpretability 89

  • 5.2.3 OPPORTUNITIES 90

    • 5.2.3.1 Enhanced language translation and localization with use of LLMs 90
    • 5.2.3.2 Emotion recognition and sentiment analysis using LLMs 90
    • 5.2.3.3 Pressing demand for LLMs in knowledge discovery and management 91

  • 5.2.4 CHALLENGES 91

    • 5.2.4.1 High inference latency 91
    • 5.2.4.2 Computational inefficiency due to large memory requirements 92
    • 5.2.4.3 Maintaining model performance and integrity 92

• 5.3 EVOLUTION OF LARGE LANGUAGE MODEL MARKET 93

• 5.4 LARGE LANGUAGE MODELS: SOFTWARE LAYERS 95

  • 5.4.1 EMBEDDING LAYER 95

  • 5.4.2 FEEDFORWARD LAYER 95

  • 5.4.3 RECURRENT LAYER 96

  • 5.4.4 ATTENTION LAYER 96

• 5.5 VALUE CHAIN ANALYSIS 97

• 5.6 ECOSYSTEM ANALYSIS/MARKET MAP 99

  • 5.6.1 LARGE LANGUAGE MODEL SOFTWARE PROVIDERS 101

    • 5.6.1.1 LLM API Providers 102
    • 5.6.1.2 Vector Database Providers 102
    • 5.6.1.3 LLM Framework Providers 102
    • 5.6.1.4 Text-to-Speech Providers 102
    • 5.6.1.5 LLM Monitoring Tools Providers 102

  • 5.6.2 LARGE LANGUAGE MODEL SERVICE PROVIDERS 103

    • 5.6.2.1 Compute Platform Providers 103
    • 5.6.2.2 Model Hubs 103
    • 5.6.2.3 Fine Tuning/Custom Model Training Frameworks 103
    • 5.6.2.4 Monitoring/Observability Platform Providers 104
    • 5.6.2.5 Hosting Service Providers 104

  • 5.6.3 END USERS 104

  • 5.6.4 GOVERNMENT & REGULATORY BODIES 104

• 5.7 INVESTMENT LANDSCAPE AND FUNDING SCENARIO 105

• 5.8 CASE STUDY ANALYSIS 109

  • 5.8.1 BFSI 109

    • 5.8.1.1 Accelerated collection and analysis of investment information for Edger Finance with generative AI 109

  • 5.8.2 MEDIA & ENTERTAINMENT 109

    • 5.8.2.1 Revolutionized decentralized digital world of media & entertainment industry for Ben Group 109

  • 5.8.3 HEALTHCARE & LIFE SCIENCES 110

    • 5.8.3.1 Summer Health reimagined pediatric doctor’s visits with OpenAI 110

  • 5.8.4 IT/ITES 110

    • 5.8.4.1 Oxide AI piloted IBM watsonx.ai to take on investment information overload in finance 110

  • 5.8.5 LAW FIRMS 111

    • 5.8.5.1 Manz leveraged Deepset cloud to significantly reduce legal research efforts through semantic search 111

• 5.9 TECHNOLOGY ANALYSIS 111

  • 5.9.1 KEY TECHNOLOGIES 111

    • 5.9.1.1 Natural Language Processing (NLP) 111
    • 5.9.1.2 Deep Learning 112
    • 5.9.1.3 Transformer Architecture 112
    • 5.9.1.4 Attention Mechanisms 112
    • 5.9.1.5 Transfer Learning 112

  • 5.9.2 ADJACENT TECHNOLOGIES 113

    • 5.9.2.1 Speech Recognition 113
    • 5.9.2.2 Computer Vision 113
    • 5.9.2.3 Reinforcement Learning 113
    • 5.9.2.4 Knowledge Graphs 114

  • 5.9.3 COMPLEMENTARY TECHNOLOGIES 114

    • 5.9.3.1 Quantum Computing 114
    • 5.9.3.2 Explainable AI 114
    • 5.9.3.3 Edge Computing 115
    • 5.9.3.4 Blockchain 115

• 5.10 REGULATORY LANDSCAPE 115

  • 5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 115

  • 5.10.2 REGULATIONS: LARGE LANGUAGE MODEL MARKET 118

    • 5.10.2.1 North America 118
      • 5.10.2.1.1 California Consumer Privacy Act (CCPA) 118
      • 5.10.2.1.2 Canada's Directive on Automated Decision-Making 118
      • 5.10.2.1.3 AI and Automated Decision Systems (AADS) Ordinance (New York City) 119
    • 5.10.2.2 Europe 119
      • 5.10.2.2.1 General Data Protection Regulation (GDPR) 119
      • 5.10.2.2.2 European Union's Artificial Intelligence Act (AIA) 119
      • 5.10.2.2.3 Ethical Guidelines for Trustworthy AI by the European Commission 119
    • 5.10.2.3 Asia Pacific 119
      • 5.10.2.3.1 Personal Information Protection Law (PIPL) - China 119
      • 5.10.2.3.2 Artificial Intelligence Ethics Guidelines - Japan 119
      • 5.10.2.3.3 AI Strategy and Governance Framework - Australia 119
    • 5.10.2.4 Middle East & Africa 120
      • 5.10.2.4.1 UAE AI Regulation and Ethics Guidelines 120
      • 5.10.2.4.2 South Africa's Protection of Personal Information Act (POPIA) 120
      • 5.10.2.4.3 Egypt's Data Protection Law 120
    • 5.10.2.5 Latin America 120
      • 5.10.2.5.1 Brazil - General Data Protection Law (LGPD) 120
      • 5.10.2.5.2 Mexico - Federal Law on the Protection of Personal Data Held by Private Parties (LFPDPPP) 120
      • 5.10.2.5.3 Argentina - Personal Data Protection Law (PDPL) 120

• 5.11 PATENT ANALYSIS 121

  • 5.11.1 METHODOLOGY 121

  • 5.11.2 PATENTS FILED, BY DOCUMENT TYPE 121

  • 5.11.3 INNOVATION AND PATENT APPLICATIONS 121

    • 5.11.3.1 Top patent owners in large language model market 122

• 5.12 PRICING ANALYSIS 126

  • 5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY SOFTWARE TYPE 128

  • 5.12.2 INDICATIVE PRICING ANALYSIS, BY OFFERING 129

• 5.13 TRADE ANALYSIS 131

  • 5.13.1 EXPORT SCENARIO OF COMPUTER SOFTWARE 131

  • 5.13.2 IMPORT SCENARIO OF COMPUTER SOFTWARE 132

• 5.14 KEY CONFERENCES & EVENTS 133

• 5.15 PORTER’S FIVE FORCES ANALYSIS 134

  • 5.15.1 THREAT OF NEW ENTRANTS 135

  • 5.15.2 THREAT OF SUBSTITUTES 135

  • 5.15.3 BARGAINING POWER OF SUPPLIERS 136

  • 5.15.4 BARGAINING POWER OF BUYERS 136

  • 5.15.5 INTENSITY OF COMPETITIVE RIVALRY 136

• 5.16 TECHNOLOGY ROADMAP 137

• 5.17 BUSINESS MODELS 138

  • 5.17.1 SOFTWARE VENDOR MODEL 139

  • 5.17.2 CLOUD API ACCESS MODEL 139

  • 5.17.3 CUSTOM TRAINING/FINE-TUNING MODEL 140

  • 5.17.4 MARKETPLACES/EXCHANGES MODEL 141

• 5.18 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 141

  • 5.18.1 REVENUE SHIFT & NEW REVENUE POCKETS FOR LARGE LANGUAGE MODEL PROVIDERS 141

• 5.19 KEY STAKEHOLDERS & BUYING CRITERIA 142

  • 5.19.1 KEY STAKEHOLDERS IN BUYING PROCESS 142

  • 5.19.2 BUYING CRITERIA 143

6 LARGE LANGUAGE MODEL MARKET, BY OFFERING 144

• 6.1 INTRODUCTION 145

  • 6.1.1 OFFERING: LARGE LANGUAGE MODEL MARKET DRIVERS 145

• 6.2 SOFTWARE, BY TYPE 147

  • 6.2.1 GENERAL PURPOSE LLMS 148

    • 6.2.1.1 High versatility and rapid adaptability of general-purpose LLMs to spur widespread deployment across multiple use cases 148

  • 6.2.2 DOMAIN-SPECIFIC LLMS 150

    • 6.2.2.1 Adoption of domain-specific LLMs to be driven by organizations' pressing needs for LLMs tailored to their industry 150
    • 6.2.2.2 Zero-shot 151
    • 6.2.2.3 One-shot 152
    • 6.2.2.4 Few-shot 153

  • 6.2.3 MULTILINGUAL LLMS 155

    • 6.2.3.1 Increasing globalization and drive for inclusivity to catalyze demand for seamless multilingual language processing capabilities 155

  • 6.2.4 TASK-SPECIFIC LLMS 156

    • 6.2.4.1 Demand for high accuracy and precision in task-oriented language models for mission-critical applications to drive task-specific LLM growth 156

• 6.3 SOFTWARE, BY SOURCE CODE 157

  • 6.3.1 OPEN-SOURCE LLMS 159

    • 6.3.1.1 Availability of powerful open-source LLM models, combined with collaborative nature of open-source community, to drive adoption and growth of open-source LLMs 159

  • 6.3.2 CLOSED-SOURCE LLMS 160

    • 6.3.2.1 Closed-source LLMs to be driven by need for customized, domain-specific AI solutions, as well as desire for exclusive access and proprietary advantages 160

• 6.4 SOFTWARE, BY DEPLOYMENT MODE 162

  • 6.4.1 CLOUD 163

    • 6.4.1.1 Increasing demand for scalable and easily accessible LLM APIs to drive growth of cloud-based LLMs 163

  • 6.4.2 ON-PREMISES 165

    • 6.4.2.1 Pressing need from enterprises for stringent data privacy and handling data in sensitive domains to fuel growth of on-premises LLMs for localized deployment and control 165

• 6.5 SERVICES 166

  • 6.5.1 CONSULTING 168

    • 6.5.1.1 Growing demand for expert guidance on LLM adoption and implementation strategies to drive consulting services segment 168

  • 6.5.2 LLM DEVELOPMENT 169

    • 6.5.2.1 LLM development services to be buoyed by increasing need for custom LLMs tailored to specific use cases and industries 169

  • 6.5.3 INTEGRATION 171

    • 6.5.3.1 Necessity of seamlessly incorporating LLMs into existing software ecosystems and workflows to accentuate demand for integration services 171

  • 6.5.4 LLM FINE-TUNING 172

    • 6.5.4.1 Domain-specific and task-specific LLM optimization set to expand LLM fine-tuning services market 172
    • 6.5.4.2 Full fine-tuning 173
    • 6.5.4.3 Retrieval-augmented Generation (RAG) 174
    • 6.5.4.4 Adapter-based parameter-efficient tuning 176

  • 6.5.5 LLM-BACKED APP DEVELOPMENT 177

    • 6.5.5.1 Adoption of LLM-backed app development services on the rise, attributed to growing popularity of LLM-powered applications across various sectors 177

  • 6.5.6 PROMPT ENGINEERING 179

    • 6.5.6.1 Prompt engineering services slated for rapid growth owing to criticality of effective prompt design for optimal LLM performance 179

  • 6.5.7 SUPPORT & MAINTENANCE 180

    • 6.5.7.1 Ongoing need for LLM model updates, monitoring, and long-term support to drive adoption of support & maintenance services 180

7 LARGE LANGUAGE MODEL MARKET, BY ARCHITECTURE 182

• 7.1 INTRODUCTION 183

  • 7.1.1 ARCHITECTURE: LARGE LANGUAGE MODEL MARKET DRIVERS 183

• 7.2 AUTOREGRESSIVE LANGUAGE MODELS 185

  • 7.2.1 AUTOREGRESSIVE LANGUAGE MODELS TO CAPTURE COMPLEX LINGUISTIC PATTERNS AND DEPENDENCIES WITHIN GIVEN CONTEXT 185

  • 7.2.2 SINGLE-HEADED AUTOREGRESSIVE LANGUAGE MODELS 186

  • 7.2.3 MULTI-HEADED AUTOREGRESSIVE LANGUAGE MODELS 186

• 7.3 AUTOENCODING LANGUAGE MODELS 187

  • 7.3.1 AELMS TO EXTEND TO FINE-TUNING DOWNSTREAM TASKS, LEADING TO WIDESPREAD ADOPTION IN INDUSTRY AND ACADEMIA 187

  • 7.3.2 VANILLA AUTOENCODING LANGUAGE MODELS 188

  • 7.3.3 OPTIMIZED AUTOENCODING LANGUAGE MODELS 188

• 7.4 HYBRID LANGUAGE MODELS 189

  • 7.4.1 INTEGRATING AUTOREGRESSIVE AND AUTOENCODING COMPONENTS TO ENABLE THEM TO EXCEL IN TASKS REQUIRING CONTEXTUAL UNDERSTANDING AND GENERATIVE CAPABILITIES 189

  • 7.4.2 TEXT-TO-TEXT LANGUAGE MODELS 190

  • 7.4.3 PRETRAINING-FINETUNING MODELS 190

8 LARGE LANGUAGE MODEL MARKET, BY MODALITY 191

• 8.1 INTRODUCTION 192

  • 8.1.1 MODALITY: LARGE LANGUAGE MODEL MARKET DRIVERS 192

• 8.2 TEXT 193

  • 8.2.1 LLMS TO LEVERAGE ADVANCED TECHNIQUES SUCH AS ATTENTION MECHANISMS AND TRANSFORMER ARCHITECTURES TO PROCESS AND GENERATE TEXT 193

• 8.3 CODE 194

  • 8.3.1 LLMS WITH CODE UNDERSTANDING CAPABILITIES TO ASSIST IN TASKS SUCH AS SOFTWARE MAINTENANCE, REFACTORING, AND OPTIMIZATION 194

• 8.4 IMAGE 195

  • 8.4.1 MULTIMODAL TRANSFORMERS AND FUSION MECHANISMS TO ALLOW LLMS TO INCORPORATE VISUAL INFORMATION ALONGSIDE TEXTUAL INPUTS 195

• 8.5 VIDEO 196

  • 8.5.1 VIDEO MODALITY TO EMPOWER LLMS TO EXTRACT MEANINGFUL INSIGHTS FROM VIDEO CONTENT, INCLUDING OBJECT RECOGNITION, ACTIVITY DETECTION, AND SCENE UNDERSTANDING 196

9 LARGE LANGUAGE MODEL MARKET, BY MODEL SIZE 198

• 9.1 INTRODUCTION 199

  • 9.1.1 MODEL SIZE: LARGE LANGUAGE MODEL MARKET DRIVERS 199

• 9.2 BELOW 1 BILLION PARAMETERS 201

  • 9.2.1 MODELS BELOW 1 BILLION PARAMETERS TO BE IDEAL FOR APPLICATIONS IN ENVIRONMENTS WITH LIMITED COMPUTATIONAL CAPABILITIES 201

• 9.3 1 BILLION TO 10 BILLION PARAMETERS 203

  • 9.3.1 MODELS BETWEEN 1 BILLION AND 10 BILLION PARAMETERS ADEPT FOR BROAD SPECTRUM OF NLP TASKS WITH MODERATE COMPUTATIONAL REQUIREMENTS 203

• 9.4 10 BILLION TO 50 BILLION PARAMETERS 205

  • 9.4.1 10 BILLION TO 50 BILLION PARAMETERS MODELS TO HANDLE ADVANCED LANGUAGE UNDERSTANDING WITHOUT RESOURCE-INTENSIVE NATURE OF LARGER MODELS 205

• 9.5 50 BILLION TO 100 BILLION PARAMETERS 207

  • 9.5.1 EXPANSION IN MODELS WITH 50 BILLION TO 100 BILLION PARAMETERS PROPELLED BY THEIR INCREASED CAPACITY FOR NUANCED LANGUAGE COMPREHENSION AND CONTEXT AWARENESS 207

• 9.6 100 BILLION TO 200 BILLION PARAMETERS 209

  • 9.6.1 HEIGHTENED CAPABILITY TO UNDERSTAND COMPLEX LANGUAGE PATTERNS TO MAKE THESE MODEL SIZES SUITABLE FOR DEVELOPING DOMAIN-SPECIFIC LLMS 209

• 9.7 200 BILLION TO 500 BILLION PARAMETERS 211

  • 9.7.1 THESE MODELS DEMONSTRATE ROBUST PERFORMANCE IN EXTENSIVE CONTEXT AWARENESS, SOPHISTICATED DIALOGUE SYSTEMS, AND ADVANCED CONTENT GENERATION 211

• 9.8 ABOVE 500 BILLION PARAMETERS 212

  • 9.8.1 MODELS ABOVE 500 BILLION PARAMETERS TO PROVIDE UNPARALLELED PERFORMANCE AND EXCEPTIONALLY HIGH LEVEL OF CONTEXT COMPREHENSION AND GENERATION 212

10 LARGE LANGUAGE MODEL MARKET, BY APPLICATION 215

• 10.1 INTRODUCTION 216

  • 10.1.1 APPLICATION: LARGE LANGUAGE MODEL MARKET DRIVERS 216

• 10.2 INFORMATION RETRIEVAL 218

  • 10.2.1 LLMS TO HELP UNDERSTAND SEMANTIC MEANING AND CONTEXT OF TEXT, ENABLING MORE ACCURATE AND RELEVANT INFORMATION RETRIEVAL 218

• 10.3 LANGUAGE TRANSLATION & LOCALIZATION 219

  • 10.3.1 LLMS TO STREAMLINE LOCALIZATION PROCESS BY AUTOMATING VARIOUS TASKS, SUCH AS TRANSLATING WEBSITE CONTENT, PRODUCT DESCRIPTIONS, AND USER INTERFACES 219

  • 10.3.2 MULTILINGUAL TRANSLATION 220

  • 10.3.3 LOCALIZATION SERVICES 221

• 10.4 CONTENT GENERATION & CURATION 221

  • 10.4.1 BUSINESSES AND CONTENT CREATORS TO STREAMLINE CONTENT WORKFLOWS, IMPROVE CONTENT QUALITY, AND DELIVER MORE PERSONALIZED EXPERIENCE 221

  • 10.4.2 AUTOMATED JOURNALISM AND ARTICLE WRITING 222

  • 10.4.3 CREATIVE WRITING 223

• 10.5 CODE GENERATION 223

  • 10.5.1 LLMS TO FACILITATE CODE REFACTORING AND OPTIMIZATION BY ANALYZING EXISTING CODEBASES AND SUGGESTING IMPROVEMENTS OR ALTERNATIVE IMPLEMENTATIONS 223

• 10.6 CUSTOMER SERVICE AUTOMATION 224

  • 10.6.1 INTEGRATING AUTOREGRESSIVE AND AUTOENCODING COMPONENTS TO ENABLE TO EXCEL IN TASKS REQUIRING CONTEXTUAL UNDERSTANDING AND GENERATIVE CAPABILITIES 224

  • 10.6.2 CHATBOTS AND VIRTUAL ASSISTANTS 225

  • 10.6.3 SALES AND MARKETING AUTOMATION 226

  • 10.6.4 PERSONALIZED RECOMMENDATION 226

• 10.7 DATA ANALYSIS AND BI 227

  • 10.7.1 INTEGRATING LLMS TO AUTOMATE DATA CATEGORIZATION, ANOMALY DETECTION, AND PREDICTIVE ANALYTICS, ENABLING BUSINESSES TO GAIN DEEPER INSIGHTS 227

  • 10.7.2 SENTIMENT ANALYSIS 228

  • 10.7.3 BUSINESS REPORTING AND MARKET ANALYSIS 228

• 10.8 OTHER APPLICATIONS 229

11 LARGE LANGUAGE MODEL MARKET, BY END USER 231

• 11.1 INTRODUCTION 232

  • 11.1.1 END USER: LARGE LANGUAGE MODEL MARKET DRIVERS 232

• 11.2 IT & ITES 234

  • 11.2.1 EXTENSIVE UTILIZATION OF LLMS IN SOFTWARE DEVELOPMENT PROCESSES AND CUSTOMER SERVICE 234

• 11.3 HEALTHCARE & LIFE SCIENCES 237

  • 11.3.1 LEVERAGING LLMS FOR SENTIMENT ANALYSIS OF PATIENT FEEDBACK, PROCESS OF LITERATURE REVIEW, AND PARSING COMPLEX DATASETS 237

• 11.4 LAW FIRMS 241

  • 11.4.1 NLP CAPABILITIES OF LLMS ENABLE LAW FIRMS TO EXTRACT INSIGHTS FROM VAST AMOUNTS OF LEGAL TEXTS 241

• 11.5 BFSI 244

  • 11.5.1 LLMS HELP FINANCIAL INSTITUTIONS AUTOMATE ROUTINE TASKS SUCH AS CONTRACT ANALYSIS AND COMPLIANCE MONITORING 244

• 11.6 MANUFACTURING 247

  • 11.6.1 OPTIMIZING SUPPLY CHAIN BY ANALYZING MARKET TRENDS, DEMAND FORECASTS, AND LOGISTICAL DATA 247

• 11.7 EDUCATION 251

  • 11.7.1 DEMAND FOR ASSISTANCE IN GRADING ASSIGNMENTS, GENERATING QUIZZES, AND PROVIDING FEEDBACK IN EDUCATION 251

• 11.8 RETAIL & ECOMMERCE 254

  • 11.8.1 NEED FOR RETAILERS TO DELIVER ENGAGING AND RESPONSIVE INTERACTIONS ACROSS MULTIPLE CHANNELS 254

• 11.9 MEDIA & ENTERTAINMENT 258

  • 11.9.1 ENABLING MEDIA COMPANIES TO OPTIMIZE CONTENT FOR DIFFERENT PLATFORMS AND AUDIENCES 258

• 11.10 OTHER END USERS 261

12 LARGE LANGUAGE MODEL MARKET, BY REGION 264

• 12.1 INTRODUCTION 265

• 12.2 NORTH AMERICA 267

  • 12.2.1 NORTH AMERICA: LARGE LANGUAGE MODEL MARKET DRIVERS 267

  • 12.2.2 NORTH AMERICA: RECESSION IMPACT 268

  • 12.2.3 US 277

    • 12.2.3.1 Supportive regulatory environment and robust technological infrastructure 277

  • 12.2.4 CANADA 278

    • 12.2.4.1 Leveraging AI-driven LLM technologies to address societal challenges and drive innovation across industries 278

• 12.3 EUROPE 279

  • 12.3.1 EUROPE: LARGE LANGUAGE MODEL MARKET DRIVERS 279

  • 12.3.2 EUROPE: RECESSION IMPACT 280

  • 12.3.3 UK 288

    • 12.3.3.1 Government support for AI development through initiatives such as AI Sector Deal, boosting investment in AI research 288

  • 12.3.4 GERMANY 289

    • 12.3.4.1 Increasing interest in developing AI solutions tailored to German language and specific regional dialects 289

  • 12.3.5 FRANCE 290

    • 12.3.5.1 Country’s combination of technological innovation, government initiatives, and thriving AI ecosystem 290

  • 12.3.6 REST OF EUROPE 291

• 12.4 ASIA PACIFIC 292

  • 12.4.1 ASIA PACIFIC: LARGE LANGUAGE MODEL MARKET DRIVERS 292

  • 12.4.2 ASIA PACIFIC: RECESSION IMPACT 293

  • 12.4.3 CHINA 302

    • 12.4.3.1 Integration of LLMs into various industries in China 302

  • 12.4.4 INDIA 303

    • 12.4.4.1 Emerging tech industry and huge digital population 303

  • 12.4.5 JAPAN 304

    • 12.4.5.1 Rich cultural heritage and technologically advanced society 304

  • 12.4.6 SOUTH KOREA 305

    • 12.4.6.1 Homegrown startup achievements on Open LLM Leaderboard indicating growing presence of Korean companies in global AI landscape 305

  • 12.4.7 REST OF ASIA PACIFIC 306

• 12.5 MIDDLE EAST & AFRICA 307

  • 12.5.1 MIDDLE EAST & AFRICA: LARGE LANGUAGE MODEL MARKET DRIVERS 307

  • 12.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT 308

  • 12.5.3 GCC 316

    • 12.5.3.1 Significant investments in AI technologies and adoption of Arabic-specific language models 316

  • 12.5.4 SOUTH AFRICA 317

    • 12.5.4.1 Strategic geographical location of South Africa and status as regional economic hub 317

  • 12.5.5 TURKEY 318

    • 12.5.5.1 Growth of Turkish economy led to greater demand for large language model services 318

  • 12.5.6 REST OF MIDDLE EAST & AFRICA 319

• 12.6 LATIN AMERICA 320

  • 12.6.1 LATIN AMERICA: LARGE LANGUAGE MODEL MARKET DRIVERS 320

  • 12.6.2 LATIN AMERICA: RECESSION IMPACT 320

  • 12.6.3 BRAZIL 328

    • 12.6.3.1 Economy driven by industries such as agriculture, manufacturing, and services to fuel need for advanced language technologies 328

  • 12.6.4 MEXICO 329

    • 12.6.4.1 Government and digital transformation initiatives to fuel adoption of advanced AI technologies 329

  • 12.6.5 REST OF LATIN AMERICA 330

13 COMPETITIVE LANDSCAPE 332

• 13.1 OVERVIEW 332

• 13.2 KEY PLAYER STRATEGIES 332

• 13.3 REVENUE ANALYSIS 334

• 13.4 MARKET SHARE ANALYSIS 335

  • 13.4.1 MARKET RANKING ANALYSIS 336

• 13.5 PRODUCT COMPARATIVE ANALYSIS 339

  • 13.5.1 PRODUCT COMPARATIVE ANALYSIS, BY GENERAL-PURPOSE LLM 339

    • 13.5.1.1 GPT-4 by OpenAI 339
    • 13.5.1.2 Llama 2 by Meta 339
    • 13.5.1.3 Claude 3 By Anthropic 339
    • 13.5.1.4 Amazon Titan by Amazon 340
    • 13.5.1.5 Gemini by Google 340

  • 13.5.2 PRODUCT COMPARATIVE ANALYSIS, BY DOMAIN-SPECIFIC LLM 340

    • 13.5.2.1 Med-PaLM by Google 340
    • 13.5.2.2 Amazon CodeWhisperer by Amazon 341
    • 13.5.2.3 BloombergGPT by Bloomberg 341
    • 13.5.2.4 FinGPT 341
    • 13.5.2.5 BioBERTpt 341

  • 13.5.3 PRODUCT COMPARATIVE ANALYSIS, BY OPEN-SOURCE LLM 342

    • 13.5.3.1 Neural Chat 7B by Intel 343
    • 13.5.3.2 Falcon by Technology Innovation Institute 343
    • 13.5.3.3 Bloom by BigScience 343
    • 13.5.3.4 Xgen 7B 4K Base by Salesforce 343
    • 13.5.3.5 Neox GPT by EleutherAI 343
    • 13.5.3.6 Abacus Giraffe by Abacus AI 343
    • 13.5.3.7 Baichuan 13B by Baichuan Intelligence 344
    • 13.5.3.8 Open Llama by OpenLM 344
    • 13.5.3.9 Vicuna-33B by Lmsys 344
    • 13.5.3.10 Dolly 2.0 by Databricks 344

• 13.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS 345

• 13.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 346

  • 13.7.1 STARS 346

  • 13.7.2 EMERGING LEADERS 346

  • 13.7.3 PERVASIVE PLAYERS 346

  • 13.7.4 PARTICIPANTS 346

  • 13.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 348

    • 13.7.5.1 Company Footprint 348
    • 13.7.5.2 Region Footprint 348
    • 13.7.5.3 Application Footprint 349
    • 13.7.5.4 End-user Footprint 350
    • 13.7.5.5 Modality Footprint 351

• 13.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 351

  • 13.8.1 PROGRESSIVE COMPANIES 352

  • 13.8.2 RESPONSIVE COMPANIES 352

  • 13.8.3 DYNAMIC COMPANIES 352

  • 13.8.4 STARTING BLOCKS 352

  • 13.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 353

    • 13.8.5.1 Detailed List of Key Startups/SMEs 353
    • 13.8.5.2 Competitive Benchmarking of Key Startups/SMEs 355

• 13.9 COMPETITIVE SCENARIO AND TRENDS 355

  • 13.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS 355

  • 13.9.2 DEALS 360

14 COMPANY PROFILES 366

• 14.1 INTRODUCTION 366

• 14.2 KEY PLAYERS 366

  • 14.2.1 GOOGLE 366

  • 14.2.2 OPENAI 372

  • 14.2.3 MICROSOFT 377

  • 14.2.4 ANTHROPIC 381

  • 14.2.5 META 384

  • 14.2.6 NVIDIA 388

  • 14.2.7 IBM 393

  • 14.2.8 HPE 397

  • 14.2.9 ORACLE 400

  • 14.2.10 AWS 404

  • 14.2.11 TENCENT 408

  • 14.2.12 YANDEX 409

  • 14.2.13 NAVER 410

  • 14.2.14 AI21 LABS 411

  • 14.2.15 HUGGING FACE 412

  • 14.2.16 BAIDU 413

  • 14.2.17 SENSETIME 414

  • 14.2.18 HUAWEI 415

• 14.3 STARTUP/SME PROFILES 416

  • 14.3.1 FEDML 416

  • 14.3.2 DYNAMOFL 416

  • 14.3.3 TOGETHER AI 417

  • 14.3.4 UPSTAGE 417

  • 14.3.5 MISTRAL AI 418

  • 14.3.6 ADEPT 419

  • 14.3.7 NEURALFINITY 420

  • 14.3.8 MOSAIC ML 421

  • 14.3.9 STABILITY AI 422

  • 14.3.10 LIGHTON 423

  • 14.3.11 COHERE 424

  • 14.3.12 TURING 425

  • 14.3.13 LIGHTNING AI 426

  • 14.3.14 WHYLABS 427

15 ADJACENT AND RELATED MARKETS 428

• 15.1 INTRODUCTION 428

• 15.2 ARTIFICIAL INTELLIGENCE MARKET - GLOBAL FORECAST TO 2030 428

  • 15.2.1 MARKET DEFINITION 428

  • 15.2.2 MARKET OVERVIEW 428

    • 15.2.2.1 Artificial intelligence market, by offering 429
    • 15.2.2.2 Artificial intelligence market, by technology 429
    • 15.2.2.3 Artificial intelligence market, by business function 430
    • 15.2.2.4 Artificial intelligence market, by vertical 431
    • 15.2.2.5 Artificial intelligence market, by region 432

• 15.3 GENERATIVE AI MARKET - GLOBAL FORECAST TO 2030 433

  • 15.3.1 MARKET DEFINITION 433

  • 15.3.2 MARKET OVERVIEW 434

    • 15.3.2.1 Generative AI market, by offering 434
    • 15.3.2.2 Generative AI market, by application 434
    • 15.3.2.3 Generative AI market, by region 436

16 APPENDIX 438

• 16.1 DISCUSSION GUIDE 438

• 16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 445

• 16.3 CUSTOMIZATION OPTIONS 447

• 16.4 RELATED REPORTS 447

• 16.5 AUTHOR DETAILS 448