Summary

The AI chip market is projected to grow from USD 203.24 billion in 2025 to USD 564.87 billion by 2032; it is expected to grow at a compound annual growth rate (CAGR) of 15.7% from 2025 to 2032. https://mnmimg.marketsandmarkets.com/Images/artificial-intelligence-chipset-market-img-overview.webp “The neural processing unit (NPU) segment is projected to record a high growth rate during the forecast period.” The neural processing unit (NPU) segment is expected to record a high growth rate in the AI chip market from 2024 to 2029. The market growth is attributed to the increasing adoption of high-end smartphones, AI PCs, and laptops, which require dedicated AI capabilities at the edge. The NPUs help accelerate neural network processing to perform AI-driven tasks, including advanced AI image processing and natural language processing. Market players are extensively focusing on developing high-end NPU solutions to stay competitive in the market. For instance, in September 2023, Apple Inc. (US) launched the iPhone 15 Pro series, featuring the A17 Pro chip. The new AI processor is equipped with a dedicated 16-core Neural Engine, capable of performing 35 trillion operations per second (TOPS). Such significant product developments and launches are expected to amplify the adoption of NPUs in the market over the forecast period. “The machine learning segment is expected to account for significant market share throughout the forecast period.” The machine learning segment of the AI chip market is expected to capture a significant market share. AI chips are crucial in processing large datasets to enable predictive analytics, supporting real-time decision-making, as they are optimized for machine learning tasks such as training and inference. For this category of AI chips, the foremost drivers of adoption were flexibility and scalability of machine learning models within autonomous systems and personalized recommendations. This AI chip is widely used in many sectors, including cloud services, healthcare, finance, automotive, and retail. Companies are developing powerful AI chips to support machine learning capabilities, which enable business insights, improve customer experience, and enhance overall efficiency. For instance, Google (US) announced Trillium in May 2024 as its sixth-generation TPU. It focuses on its cloud platform, featuring an onboard accelerator for accelerating machine learning workloads. Enterprises that have adopted TPUs widely bring machine learning power to predictive analytics, personalization, and operational efficiency. This represents increasing dependence on AI chips in this domain. As businesses seek to harness the power of data for insights, efficiencies, and enhanced customer experiences, demand is surging for machine learning capabilities. “The US is expected to hold the largest share of the North American market during the forecast period.” The US is expected to hold the largest share of the AI chip market in North America. The presence of prominent technology firms and data center operators is driving the AI chip market across North America. The region hosts companies such as NVIDIA Corporation (US), Intel Corporation (US), Advanced Micro Devices, Inc. (AMD) (US), Google (US); and cloud service providers include Amazon Web Services, Inc. (AWS) (US), Microsoft Azure (US), and Google Cloud (US). For instance, in April 2024, Google (US) announced a USD 3 billion investment to expand its data centers across the US. These data centers are further supported by AI infrastructure, enabling them to provide real-time services worldwide. The region also hosts several startups established in the area to provide AI chips for data centers, including SAPEON Inc. (US), Tenstorrent (Canada), Taalas (Canada), Kneron, Inc. (US), and SambaNova Systems, Inc. (US). North America has a well-established technological infrastructure that supports advanced AI research and development. There are many modern data centers in this region, equipped with state-of-the-art AI hardware. They may include GPUs, TPUs, and specialized AI chips. The presence of large-scale data centers and leading AI chip developers in the region is driving the growth of the AI chip market. Extensive primary interviews were conducted with key industry experts in the AI chip market to determine and verify the market size for various segments and subsegments, which were gathered through secondary research. The breakdown of primary participants for the report is shown below. The study draws insights from a range of industry experts, including component suppliers, Tier 1 companies, and OEMs. The break-up of the primaries is as follows: ? By Company Type ? Tier 1 ? 45%, Tier 2 ? 32%, and Tier 3 ? 23% ? By Designation ? C-level Executives ? 30%, Directors ? 45%, and Others ? 25% ? By Region ? Asia Pacific ? 26%, Europe ? 40%, North America ? 22%, and RoW ? 12% Note: Other designations include technology heads, media analysts, sales managers, marketing managers, and product managers. The three tiers of companies are based on their total revenues as of 2024: Tier 1: >USD 1 billion, Tier 2: USD 500 million to 1 billion, and Tier 3:

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Table of Contents

1 INTRODUCTION 30

1.1 STUDY OBJECTIVES 30

1.2 MARKET DEFINITION 30

1.3 STUDY SCOPE 31

1.3.1 MARKETS COVERED 31

1.3.2 INCLUSIONS AND EXCLUSIONS 32

1.3.3 YEARS CONSIDERED 32

1.4 CURRENCY CONSIDERED 33

1.5 LIMITATIONS 33

1.6 STAKEHOLDERS 33

1.7 SUMMARY OF CHANGES 34

 

 

2 EXECUTIVE SUMMARY 36

2.1 MARKET HIGHLIGHTS AND KEY INSIGHTS 36

2.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS 37

2.3 DISRUPTIONS SHAPING AI CHIP MARKET 38

2.4 HIGH-GROWTH SEGMENTS 38

2.5 REGIONAL SNAPSHOT: MARKET SIZE, GROWTH RATE, AND FORECAST 39

 

 

3 PREMIUM INSIGHTS 40

3.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI CHIP MARKET 40

3.2 AI CHIP MARKET, BY COMPUTE 40

3.3 AI CHIP MARKET, BY FUNCTION 41

3.4 AI CHIP MARKET, BY END USER 41

3.5 ASIA PACIFIC: AI CHIP MARKET, BY FUNCTION AND COUNTRY 42

3.6 AI CHIP MARKET, BY COUNTRY 42

 

 

4 MARKET OVERVIEW 43

4.1 INTRODUCTION 43

4.2 MARKET DYNAMICS 43

4.2.1 DRIVERS 44

4.2.1.1 Pressing need for large-scale data handling and real-time analytics 44

4.2.1.2 Rising adoption of autonomous vehicles 45

4.2.1.3 Surging use of GPUs and ASICs in AI servers 45

4.2.1.4 Continuous advancements in machine learning and deep learning technologies 46

4.2.1.5 Increasing penetration of AI servers 46

4.2.2 RESTRAINTS 47

4.2.2.1 Shortage of skilled workforce with technical know-how 47

4.2.2.2 Computational workloads and power consumption in AI chips 48

4.2.2.3 Unreliability of AI algorithms 49

4.2.3 OPPORTUNITIES 50

4.2.3.1 Elevating demand for AI-based FPGA chips 50

4.2.3.2 Government initiatives to deploy AI-enabled defense systems 51

4.2.3.3 Growing trend of AI-driven diagnostics and treatments 51

4.2.3.4 Increasing investments in AI-enabled data centers by cloud service providers 52

4.2.3.5 Rising popularity of AI-based ASIC technology 52

4.2.4 CHALLENGES 53

4.2.4.1 Data privacy concerns associated with AI platforms 53

4.2.4.2 Availability of limited structured data to develop efficient AI systems 54

4.2.4.3 Supply chain disruptions 54

4.3 UNMET NEEDS AND WHITE SPACES 55

4.3.1 UNMET NEEDS IN AI CHIPS MARKET 55

4.4 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES 56

4.4.1 INTERCONNECTED MARKETS 56

4.4.2 CROSS-SECTOR OPPORTUNITIES 58

4.5 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS 59

4.5.1 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS 59

 

 

5 INDUSTRY TRENDS 60

5.1 INTRODUCTION 60

5.2 PORTER’S FIVE FORCES ANALYSIS 60

5.2.1 THREAT OF NEW ENTRANTS 61

5.2.2 THREAT OF SUBSTITUTES 61

5.2.3 BARGAINING POWER OF SUPPLIERS 62

5.2.4 BARGAINING POWER OF BUYERS 62

5.2.5 INTENSITY OF COMPETITIVE RIVALRY 62

5.3 MACROECONOMICS INDICATORS 63

5.3.1 INTRODUCTION 63

5.3.2 GDP TRENDS AND FORECAST 63

5.3.3 TRENDS IN AI CHIP MARKET 64

5.4 VALUE CHAIN ANALYSIS 65

5.5 ECOSYSTEM ANALYSIS 67

5.6 PRICING ANALYSIS 70

5.6.1 AVERAGE SELLING PRICE OF COMPUTE, BY KEY PLAYERS 70

5.6.2 AVERAGE SELLING PRICE TREND, BY REGION 71

5.7 TRADE ANALYSIS 73

5.7.1 IMPORT DATA (HS CODE 854231) 73

5.7.2 EXPORT SCENARIO (HS CODE 854231) 74

5.8 KEY CONFERENCES AND EVENTS, 2026 75

5.9 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 76

5.10 INVESTMENT AND FUNDING SCENARIO 76

5.11 CASE STUDY ANALYSIS 77

5.11.1 CDW INTEGRATED AMD EPYC SOLUTIONS TO ENSURE ENERGY EFFICIENCY AND OPTIMUM SPACE UTILIZATION 77

5.11.2 OVH SAS LEVERAGED AMD EPYC PROCESSOR TO OPTIMIZE PERFORMANCE OF CLOUD SOLUTIONS IN AI WORKLOADS 77

5.11.3 INTEL XEON SCALABLE PROCESSORS POWER TENCENT CLOUD’S XIAOWEI INTELLIGENT SPEECH AND VIDEO SERVICE ACCESS PLATFORM 78

5.11.4 AIC HELPS WESTERN DIGITAL TO ENHANCE SSD TESTING AND VALIDATION EFFICIENCY USING AMD PROCESSOR 78

5.12 IMPACT OF 2025 US TARIFFS - AI CHIP MARKET 79

5.12.1 KEY TARIFF RATES 80

5.12.2 PRICE IMPACT ANALYSIS 81

5.12.3 IMPACT ON REGIONS/COUNTRIES 81

5.12.3.1 US 81

5.12.3.2 Europe 82

5.12.3.3 Asia Pacific 83

5.12.4 IMPACT ON END USERS 84

5.12.4.1 Consumers 84

5.12.4.2 Data centers 84

5.12.4.3 Other organizations 84

5.13 SERVER COST STRUCTURE/BILL OF MATERIALS 85

5.13.1 CPU SERVER 85

5.13.2 GPU SERVER 87

5.14 PENETRATION AND GROWTH OF AI SERVERS 90

5.15 UPCOMING DEPLOYMENT OF DATA CENTERS BY CLOUD SERVICE PROVIDERS (CSPS) 90

5.16 CAPEX OF CLOUD SERVICE PROVIDERS 91

5.17 SERVER PROCUREMENT BY CLOUD SERVICE PROVIDERS 92

5.18 PROCESSOR BENCHMARKING 93

5.18.1 GPU BENCHMARKING 93

5.18.2 CPU BENCHMARKING 93

 

6 TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS 94

6.1 TECHNOLOGY ANALYSIS 94

6.1.1 KEY TECHNOLOGIES 94

6.1.1.1 High-bandwidth memory (HBM) 94

6.1.1.2 GenAI workload 95

6.1.2 COMPLEMENTARY TECHNOLOGIES 95

6.1.2.1 Data center power management and cooling system 95

6.1.2.2 High-speed interconnects 95

6.1.3 ADJACENT TECHNOLOGIES 96

6.1.3.1 AI development frameworks 96

6.1.3.2 Quantum AI 96

6.2 TECHNOLOGY ROADMAP 96

6.3 PATENT ANALYSIS 98

6.4 FUTURE APPLICATIONS 104

 

 

7 REGULATORY LANDSCAPE 106

7.1 INTRODUCTION 106

7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106

7.3 STANDARDS 110

 

 

8 CUSTOMER LANDSCAPE AND BUYER BEHAVIOR 113

8.1 DECISION-MAKING PROCESS 113

8.2 KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA 114

8.2.1 KEY STAKEHOLDERS IN BUYING PROCESS 114

8.2.2 BUYING CRITERIA 115

8.3 ADOPTION BARRIERS AND INTERNAL CHALLENGES 116

8.4 UNMET NEEDS FROM VARIOUS VERTICALS 117

 

 

9 AI CHIP MARKET, BY COMPUTE 118

9.1 INTRODUCTION 119

9.2 GPU 122

9.2.1 ABILITY TO HANDLE AI WORKLOADS AND PROCESS VAST DATA VOLUMES TO BOOST ADOPTION 122

9.3 CPU 123

9.3.1 RISING DEMAND FOR VERSATILE AND GENERAL-PURPOSE AI PROCESSING TO AUGMENT MARKET GROWTH 123

9.4 FPGA 124

9.4.1 GROWING NEED FOR FLEXIBILITY AND CUSTOMIZATION FOR AI WORKLOADS TO SPUR DEMAND 124

9.5 NPU 125

9.5.1 RISING DEMAND FOR HIGH-END SMARTPHONES TO DRIVE SEGMENTAL GROWTH 125

9.6 TPU 126

9.6.1 PRESSING NEED FOR FASTER PROCESSING IN AI RESEARCH AND APPLICATION DEVELOPMENT TO BOOST DEMAND 126

9.7 DOJO & FSD 127

9.7.1 ACCELERATING DEMAND FOR HIGH-PERFORMANCE, ENERGY-EFFICIENT AI PROCESSING IN AUTONOMOUS VEHICLES TO FUEL ADOPTION 127

9.8 TRAINIUM & INFERENTIA 128

9.8.1 ABILITY TO TRAIN COMPLEX AI AND DEEP LEARNING MODELS TO DRIVE ADOPTION 128

9.9 ATHENA ASIC 129

9.9.1 INCREASING NEED TO HANDLE COMPLEX NLP AND LANGUAGE-BASED AI TASKS TO ACCELERATE MARKET GROWTH 129

9.10 T-HEAD 129

9.10.1 RISING DEMAND FOR CUSTOMIZED, HIGH-PERFORMANCE AI CHIPS ACROSS CHINESE DATA CENTERS TO STIMULATE MARKET GROWTH 129

9.11 MTIA 130

9.11.1 META'S EXPANSION INTO AR, VR, AND METAVERSE TO FUEL DEMAND 130

9.12 LPU 130

9.12.1 INCREASING NEED TO HANDLE COMPLEX NLP AND LANGUAGE-BASED AI TASKS TO ACCELERATE MARKET GROWTH 130

9.13 ASCEND 131

9.13.1 RISING DEMAND FOR UNIFIED AI ARCHITECTURES AND DOMESTIC CHIP ECOSYSTEMS TO DRIVE MARKET EXPANSION 131

9.14 OTHER ASICS 131

 

 

10 AI CHIP MARKET, BY MEMORY 132

10.1 INTRODUCTION 133

10.2 DDR 135

10.2.1 RISING ADOPTION OF AI-ENABLED CPUS IN DATA CENTERS TO SUPPORT MARKET GROWTH 135

10.3 HBM 136

10.3.1 ELEVATING NEED FOR HIGH THROUGHPUT IN DATA-INTENSIVE AI TASKS TO FUEL MARKET GROWTH 136

 

 

11 AI CHIP MARKET, BY NETWORK 137

11.1 INTRODUCTION 138

11.2 NIC/NETWORK ADAPTERS 140

11.2.1 INFINIBAND 141

11.2.1.1 Growing utilization of HPC and AI models to minimize latency and maximize throughput to boost segmental growth 141

11.2.2 ETHERNET 142

11.2.2.1 Rising demand for scalable and cost-effective networking solutions to propel demand 142

11.3 INTERCONNECTS 142

11.3.1 GROWING COMPLEXITY OF AI MODELS REQUIRING HIGH-BANDWIDTH DATA PATHS TO FUEL DEMAND 142

 

 

12 AI CHIP MARKET, BY TECHNOLOGY 143

12.1 INTRODUCTION 144

12.2 GENERATIVE AI 145

12.2.1 RULE-BASED MODELS 147

12.2.1.1 Rising need to detect fraud in finance sector to propel market 147

12.2.2 STATISTICAL MODELS 148

12.2.2.1 Need for accurate predictions from complex data structures to boost segmental growth 148

12.2.3 DEEP LEARNING 149

12.2.3.1 Ability to advance AI technologies to boost demand 149

12.2.4 GENERATIVE ADVERSARIAL NETWORKS (GAN) 150

12.2.4.1 Pressing need to handle large-scale data to fuel demand 150

12.2.5 AUTOENCODERS 152

12.2.5.1 Ability to compress and restructure data to ensure optimum storage space in data centers to stimulate demand 152

12.2.6 CONVOLUTIONAL NEURAL NETWORKS 153

12.2.6.1 Surging demand for realistic and high-quality images and videos to accelerate market growth 153

12.2.7 TRANSFORMER MODELS 154

12.2.7.1 Increasing utilization in image synthesis and captioning applications to foster segmental growth 154

12.3 MACHINE LEARNING 156

12.3.1 RISING USE IN IMAGE & SPEECH RECOGNITION AND PREDICTIVE ANALYTICS TO CONTRIBUTE TO MARKET GROWTH 156

12.4 NATURAL LANGUAGE PROCESSING 157

12.4.1 INCREASING NEED FOR REAL-TIME APPLICATIONS TO SUPPORT MARKET GROWTH 157

12.5 COMPUTER VISION 158

12.5.1 ESCALATING NEED FOR ADVANCED PROCESSING CAPABILITIES TO BOOST DEMAND 158

 

 

13 AI CHIP MARKET, BY FUNCTION 160

13.1 INTRODUCTION 161

13.2 TRAINING 163

13.2.1 SURGING NEED TO PROCESS LARGE DATA SETS AND PERFORM PARALLEL COMPUTATION TO CREATE GROWTH OPPORTUNITIES 163

13.3 INFERENCE 164

13.3.1 SURGING DEPLOYMENT ACROSS VARIOUS INDUSTRIES TO BOOST DEMAND 164

 

 

14 AI CHIP MARKET, BY END USER 165

14.1 INTRODUCTION 166

14.2 CONSUMERS 167

14.2.1 GROWING ADOPTION OF AI-ENABLED PERSONAL DEVICES TO PROPEL MARKET 167

14.3 DATA CENTERS 168

14.3.1 CLOUD SERVICE PROVIDERS 169

14.3.1.1 Surging AI workloads and cloud adoption to stimulate market growth 169

14.3.2 ENTERPRISES 170

14.3.2.1 Escalating use of NLP, image recognition, and predictive analytics to create growth opportunities 170

14.3.2.2 Healthcare 171

14.3.2.2.1 Integration of AI in computer-aided drug discovery and development to foster market growth 171

14.3.2.3 BFSI 172

14.3.2.3.1 Surging need for fraud detection in financial institutions to boost demand 172

14.3.2.4 Automotive 173

14.3.2.4.1 Growing focus on safe and enhanced driving experiences to fuel demand 173

14.3.2.5 Retail & e-commerce 175

14.3.2.5.1 Increasing use of chatbots and virtual assistants to offer improved customer services to drive market 175

14.3.2.6 Media & entertainment 176

14.3.2.6.1 Real-time analysis of viewer preferences, engagement patterns, and demographic information to augment market growth 176

14.3.2.7 Others 177

14.4 GOVERNMENT ORGANIZATIONS 178

14.4.1 SIGNIFICANT FOCUS ON AUTOMATING ROUTINE TASKS AND EXTRACTING REAL-TIME ACTIONABLE INSIGHTS TO SUPPORT MARKET GROWTH 178

 

 

15 AI CHIP MARKET, BY REGION 180

15.1 INTRODUCTION 181

15.2 NORTH AMERICA 182

15.2.1 US 186

15.2.1.1 Government-led initiatives to boost semiconductor manufacturing to drive market 186

15.2.2 CANADA 187

15.2.2.1 Growing emphasis on commercializing AI to spur demand 187

15.2.3 MEXICO 187

15.2.3.1 Increasing shift toward digital platforms and cloud-based solutions to accelerate demand 187

15.3 EUROPE 188

15.3.1 UK 192

15.3.1.1 Growing investments in data center infrastructure to boost demand 192

15.3.2 GERMANY 193

15.3.2.1 Robust industrial base to offer lucrative growth opportunities 193

15.3.3 FRANCE 194

15.3.3.1 Increasing number of AI startups to accelerate demand 194

15.3.4 ITALY 194

15.3.4.1 Rising trend of digitalization in automotive and healthcare sectors to drive market 194

15.3.5 SPAIN 195

15.3.5.1 Growing collaborations and partnerships among AI manufacturers to spur demand 195

15.3.6 REST OF EUROPE 195

15.4 ASIA PACIFIC 196

15.4.1 CHINA 200

15.4.1.1 Surge in research funding and implementation of supportive regulatory policy to augment market growth 200

15.4.2 JAPAN 201

15.4.2.1 Rising focus on advancing robotic systems to boost market 201

15.4.3 INDIA 202

15.4.3.1 Government-led initiatives to boost AI infrastructure to foster market growth 202

15.4.4 SOUTH KOREA 202

15.4.4.1 Thriving semiconductor industry to drive market 202

15.4.5 REST OF ASIA PACIFIC 203

15.5 REST OF THE WORLD 203

15.5.1 MIDDLE EAST 206

15.5.1.1 Growing emphasis on digital transformation and technological innovation to drive market 206

15.5.1.2 GCC countries 207

15.5.1.3 Rest of Middle East 208

15.5.2 AFRICA 208

15.5.2.1 Rising internet penetration and mobile subscriptions to drive market 208

15.5.3 SOUTH AMERICA 209

15.5.3.1 Growing need to store vast volumes of data to boost demand 209

 

 

16 COMPETITIVE LANDSCAPE 210

16.1 OVERVIEW 210

16.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021-2025 210

16.3 REVENUE ANALYSIS, 2021-2024 212

16.4 MARKET SHARE ANALYSIS, 2024 214

16.4.1 COMPUTE MARKET SHARE, 2024 214

16.4.2 MEMORY (HBM) MARKET SHARE, 2023 216

16.5 COMPANY VALUATION AND FINANCIAL METRICS 218

16.6 BRAND/PRODUCT COMPARISON 219

16.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024 220

16.7.1 STARS 220

16.7.2 EMERGING LEADERS 220

16.7.3 PERVASIVE PLAYERS 220

16.7.4 PARTICIPANTS 220

16.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024 222

16.7.5.1 Company footprint 222

16.7.5.2 Regional footprint 223

16.7.5.3 Compute footprint 224

16.7.5.4 Memory footprint 225

16.7.5.5 Network footprint 226

16.7.5.6 Technology footprint 227

16.7.5.7 Function footprint 228

16.7.5.8 End user footprint 229

16.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024 229

16.8.1 PROGRESSIVE COMPANIES 229

16.8.2 RESPONSIVE COMPANIES 230

16.8.3 DYNAMIC COMPANIES 230

16.8.4 STARTING BLOCKS 230

16.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024 231

16.8.5.1 Detailed list of key startups/SMEs 231

16.8.5.2 Competitive benchmarking of key startups/SMEs 232

16.9 COMPETITIVE SCENARIO 233

16.9.1 PRODUCT LAUNCHES 233

16.9.2 DEALS 235

 

 

17 COMPANY PROFILES 237

17.1 KEY PLAYERS 237

17.1.1 NVIDIA CORPORATION 237

17.1.1.1 Business overview 237

17.1.1.2 Products/Solutions/Services offered 238

17.1.1.3 Recent developments 241

17.1.1.3.1 Product launches 241

17.1.1.3.2 Deals 242

17.1.1.4 MnM view 243

17.1.1.4.1 Key strengths 243

17.1.1.4.2 Strategic choices 243

17.1.1.4.3 Weaknesses and competitive threats 243

17.1.2 ADVANCED MICRO DEVICES, INC. 244

17.1.2.1 Business overview 244

17.1.2.2 Products/Solutions/Services offered 245

17.1.2.3 Recent developments 247

17.1.2.3.1 Product launches 247

17.1.2.3.2 Deals 248

17.1.2.4 MnM view 249

17.1.2.4.1 Key strengths 249

17.1.2.4.2 Strategic choices 249

17.1.2.4.3 Weaknesses and competitive threats 249

17.1.3 INTEL CORPORATION 250

17.1.3.1 Business overview 250

17.1.3.2 Products/Solutions/Services offered 251

17.1.3.3 Recent developments 254

17.1.3.3.1 Product launches 254

17.1.3.3.2 Deals 256

17.1.3.4 MnM view 257

17.1.3.4.1 Key strengths 257

17.1.3.4.2 Strategic choices 257

17.1.3.4.3 Weaknesses and competitive threats 257

17.1.4 SK HYNIX INC. 258

17.1.4.1 Business overview 258

17.1.4.2 Products/Solutions/Services offered 258

17.1.4.3 Recent developments 259

17.1.4.3.1 Product launches 259

17.1.4.3.2 Deals 260

17.1.4.3.3 Other developments 260

17.1.4.4 MnM view 260

17.1.4.4.1 Key strengths 260

17.1.4.4.2 Strategic choices 260

17.1.4.4.3 Weaknesses and competitive threats 261

17.1.5 SAMSUNG 262

17.1.5.1 Business overview 262

17.1.5.2 Products/Solutions/Services offered 263

17.1.5.3 Recent developments 266

17.1.5.3.1 Product launches 266

17.1.5.3.2 Deals 268

17.1.5.4 MnM view 269

17.1.5.4.1 Key strengths 269

17.1.5.4.2 Strategic choices 269

17.1.5.4.3 Weaknesses and competitive threats 269

17.1.6 MICRON TECHNOLOGY, INC. 270

17.1.6.1 Business overview 270

17.1.6.2 Products/Solutions/Services offered 271

17.1.6.3 Recent developments 272

17.1.6.3.1 Product launches 272

17.1.6.3.2 Deals 274

17.1.7 APPLE INC. 275

17.1.7.1 Business overview 275

17.1.7.2 Products/Solutions/Services offered 276

17.1.7.3 Recent developments 277

17.1.7.3.1 Product launches 277

17.1.7.3.2 Deals 279

17.1.8 QUALCOMM TECHNOLOGIES, INC. 280

17.1.8.1 Business overview 280

17.1.8.2 Products/Solutions/Services offered 281

17.1.8.3 Recent developments 284

17.1.8.3.1 Product launches 284

17.1.8.3.2 Deals 285

17.1.9 HUAWEI TECHNOLOGIES CO., LTD. 287

17.1.9.1 Business overview 287

17.1.9.2 Products/Solutions/Services offered 288

17.1.9.3 Recent developments 289

17.1.9.3.1 Product launches 289

17.1.9.3.2 Deals 290

17.1.10 GOOGLE 291

17.1.10.1 Business overview 291

17.1.10.2 Products/Solutions/Services offered 292

17.1.10.3 Recent developments 293

17.1.10.3.1 Product launches 293

17.1.10.3.2 Deals 294

17.1.11 AMAZON WEB SERVICES, INC. 295

17.1.11.1 Business overview 295

17.1.11.2 Products/Solutions/Services offered 296

17.1.11.3 Recent developments 296

17.1.11.3.1 Product launches 296

17.1.11.3.2 Deals 297

17.1.12 TESLA 299

17.1.12.1 Business overview 299

17.1.12.2 Products/Solutions/Services offered 300

17.1.13 MICROSOFT 301

17.1.13.1 Business overview 301

17.1.13.2 Products/Solutions/Services offered 302

17.1.13.3 Recent developments 303

17.1.13.3.1 Product launches 303

17.1.13.3.2 Deals 303

17.1.14 META 305

17.1.14.1 Business overview 305

17.1.14.2 Products/Solutions/Services offered 306

17.1.14.3 Recent developments 307

17.1.14.3.1 Product launches 307

17.1.14.3.2 Deals 307

17.1.15 T-HEAD 308

17.1.15.1 Business overview 308

17.1.15.2 Products/Solutions/Services offered 308

17.1.16 IMAGINATION TECHNOLOGIES 309

17.1.16.1 Business overview 309

17.1.16.2 Products/Solutions/Services offered 309

17.1.16.3 Recent developments 310

17.1.16.3.1 Product launches 310

17.1.16.3.2 Deals 310

17.1.17 GRAPHCORE 311

17.1.17.1 Business overview 311

17.1.17.2 Products/Solutions/Services offered 311

17.1.17.3 Recent developments 312

17.1.17.3.1 Product launches 312

17.1.17.3.2 Deals 312

17.1.18 CEREBRAS 313

17.1.18.1 Business overview 313

17.1.18.2 Products/Solutions/Services offered 313

17.1.18.3 Recent developments 313

17.1.18.3.1 Product launches 313

17.1.18.3.2 Deals 314

17.2 OTHER PLAYERS 315

17.2.1 MYTHIC 315

17.2.2 KALRAY 316

17.2.3 BLAIZE 317

17.2.4 GROQ, INC. 318

17.2.5 HAILO TECHNOLOGIES LTD 319

17.2.6 GREENWAVES TECHNOLOGIES 320

17.2.7 SIMA TECHNOLOGIES, INC. 320

17.2.8 KNERON, INC. 321

17.2.9 RAIN NEUROMORPHICS INC. 321

17.2.10 TENSTORRENT 322

17.2.11 SAMBANOVA SYSTEMS, INC. 322

17.2.12 TAALAS 323

17.2.13 SAPEON INC. 323

17.2.14 REBELLIONS INC. 324

17.2.15 RIVOS INC. 324

17.2.16 SHANGHAI BIREN TECHNOLOGY CO., LTD. 325

 

 

18 RESEARCH METHODOLOGY 326

18.1 RESEARCH DATA 326

18.1.1 SECONDARY DATA 327

18.1.1.1 List of key secondary sources 327

18.1.1.2 Key data from secondary sources 327

18.1.2 PRIMARY DATA 328

18.1.2.1 List of key interview participants 328

18.1.2.2 Breakdown of primaries 328

18.1.2.3 Key industry insights 329

18.1.3 SECONDARY AND PRIMARY RESEARCH 329

18.2 MARKET SIZE ESTIMATION 330

18.2.1 BOTTOM-UP APPROACH 331

18.2.1.1 Approach to estimate market size using bottom-up analysis (supply side) 331

18.2.2 TOP-DOWN APPROACH 332

18.2.2.1 Approach to estimate market size using top-down analysis (demand side) 332

18.3 DATA TRIANGULATION 333

18.4 RESEARCH ASSUMPTIONS 334

18.5 RISK ANALYSIS 335

18.6 RESEARCH LIMITATIONS 335

 

 

19 APPENDIX 336

19.1 DISCUSSION GUIDE 336

19.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 340

19.3 CUSTOMIZATION OPTIONS 342

19.4 RELATED REPORTS 342

19.5 AUTHOR DETAILS 343

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