The Global Machine Learning Chip Market size is expected to reach $45.0 billion by 2031, rising at a market growth of 22.0% CAGR during the forecast period.
The North America region witnessed 37% revenue share in this market in 2023. This can be attributed to the presence of major technology companies, high levels of investment in AI and machine learning, and strong demand for advanced computational power in sectors such as IT, healthcare, automotive, and finance. North America is home to leading ML chip manufacturers, startups, and research institutions, which drive innovation and the adoption of cutting-edge machine learning technologies.
The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. For instance, In October, 2024, Advanced Micro Devices Inc. unveiled the MI325x AI chip, competing with Nvidia's Blackwell series in the AI hardware market. It offers improved processing power, energy efficiency, and compatibility with open-source frameworks. Built on a 3nm process, the MI325x features RDNA4 architecture for enhanced deep learning performance. Moreover, In October, 2024, Infineon Technologies is enhancing its AI software portfolio with the launch of DEEPCRAFT, a brand for Edge AI and Machine Learning solutions. DEEPCRAFT includes existing products like DEEPCRAFT Studio and Ready Models and will expand to offer a broader range of Edge AI software, models, and solutions for diverse applications.
Additionally, the deployment of 5G networks is crucial in accelerating the demand for these chips, as 5G offers ultra-low latency, faster data transfer speeds, and higher bandwidth. These capabilities are essential for real-time machine learning applications, such as autonomous vehicles, smart cities, and augmented reality, where rapid data processing is required to make split-second decisions. Hence, the emergence of 5G networks and the need for low-latency AI processing drive the market's growth.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Product Launches and Product Expansions.
The Machine Learning Chip Market, excluding top key players, is characterized by intense competition among mid-sized and emerging companies. Innovators focus on specialized solutions, cost-effective chips, and niche applications like IoT and edge AI. Regional players leverage local manufacturing and customizations to gain an edge. Partnerships and collaborations drive growth, while barriers include R&D costs and scaling challenges.
The North America region witnessed 37% revenue share in this market in 2023. This can be attributed to the presence of major technology companies, high levels of investment in AI and machine learning, and strong demand for advanced computational power in sectors such as IT, healthcare, automotive, and finance. North America is home to leading ML chip manufacturers, startups, and research institutions, which drive innovation and the adoption of cutting-edge machine learning technologies.
The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. For instance, In October, 2024, Advanced Micro Devices Inc. unveiled the MI325x AI chip, competing with Nvidia's Blackwell series in the AI hardware market. It offers improved processing power, energy efficiency, and compatibility with open-source frameworks. Built on a 3nm process, the MI325x features RDNA4 architecture for enhanced deep learning performance. Moreover, In October, 2024, Infineon Technologies is enhancing its AI software portfolio with the launch of DEEPCRAFT, a brand for Edge AI and Machine Learning solutions. DEEPCRAFT includes existing products like DEEPCRAFT Studio and Ready Models and will expand to offer a broader range of Edge AI software, models, and solutions for diverse applications.
Cardinal Matrix - Market Competition Analysis
Based on the Analysis presented in the Cardinal matrix; NVIDIA Corporation and Amazon Web Services, Inc. are the forerunners in the Machine Learning Chip Market. Companies such as Samsung Electronics Co., Ltd., Qualcomm Incorporated, and IBM Corporation are some of the key innovators in Machine Learning Chip Market. In October, 2024, Qualcomm Incorporated unveiled the Snapdragon 8 Elite Mobile Platform, the world’s fastest mobile system-on-a-chip, featuring the second-gen Qualcomm Oryon CPU, Adreno GPU, and Hexagon NPU. These innovations enable game-changing performance, multi-modal generative AI, and enhanced camera, gaming, and browsing experiences while prioritizing user privacy and power efficiency.
Market Growth Factors
The increasing use of artificial intelligence (AI) and machine learning (ML) across various industries, including healthcare, finance, automotive, and retail, is a key driver for this market. Industries are turning to AI and ML for enhanced data analysis, automation, and decision-making capabilities, which require specialized hardware for optimal performance. In conclusion, rising demand for AI and machine learning applications across various industries drives the market's growth.Additionally, the deployment of 5G networks is crucial in accelerating the demand for these chips, as 5G offers ultra-low latency, faster data transfer speeds, and higher bandwidth. These capabilities are essential for real-time machine learning applications, such as autonomous vehicles, smart cities, and augmented reality, where rapid data processing is required to make split-second decisions. Hence, the emergence of 5G networks and the need for low-latency AI processing drive the market's growth.
Market Restraining Factors
However, One of the primary restraints this market faces is the high cost of developing and manufacturing specialized chips. Unlike general-purpose processors, these chips must be designed to handle specific tasks, such as deep learning and data-intensive computations. This often requires advanced research, significant design efforts, and costly production processes, which can make these chips prohibitively expensive for smaller companies or startups. Therefore, specialized these chips' high development and manufacturing costs hinder the market's growth.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Product Launches and Product Expansions.
Driving and Restraining Factors
Drivers
- Rising Demand For AI And Machine Learning Applications Across Various Industries
- Emergence Of 5G Networks And The Need For Low-Latency AI Processing
- Surge In Cloud Computing And Data Center Demand For High-Performance Chips
Restraints
- High Development And Manufacturing Costs Of Specialized Machine Learning Chips
- Security Concerns Related To Machine Learning Chip Vulnerabilities
Opportunities
- Increased Investment In AI Research And Development By Major Tech Companies
- Growth Of Autonomous Vehicles And The Need For Real-Time Data Processing
Challenges
- Concerns Over Energy Consumption And Sustainability
- Short Lifecycle And Rapid Technological Obsolescence
Technology Outlook
Based on technology, the machine learning chip market is divided into system-on-chip (SoC), system-in-package, multi-chip module, and others. The system-in-package segment held 25% revenue share in this market in 2023. SiP technology involves packaging multiple integrated circuits (ICs) within a single package, offering greater flexibility in design. This approach combines different chips, such as processors, memory, and sensors, into one compact unit. SiPs are particularly beneficial for edge computing, IoT devices, and portable electronics, where space and customization are crucial.Chip Type Outlook
On the basis of chip type, the machine learning chip market is segmented into GPU, ASIC, neuromorphic chip, FPGA, flash-based chip, CPU, and others. The ASIC segment held 25% revenue share in this market in 2023. ASICs are custom-designed chips optimized for specific tasks, making them highly efficient and powerful for specialized machine learning applications. Their application in data centers, autonomous vehicles, and high-frequency trading is growing, as they can significantly reduce processing time and power consumption for tasks like deep learning model inference.
Industry Vertical Outlook
By industry vertical, the machine learning chip market is divided into BFSI, IT and telecom, media and advertising, retail, healthcare, automotive, robotics industry, and others. The BFSI segment procured 13% revenue share in this market in 2023. Financial institutions increasingly leverage machine learning algorithms for fraud detection, risk management, customer personalization, and high-frequency trading. The need for real-time data processing and accurate predictive models in this industry has driven the adoption of specialized ML chips, which enhance the performance and efficiency of these computational tasks.Market Competition and Attributes
The Machine Learning Chip Market, excluding top key players, is characterized by intense competition among mid-sized and emerging companies. Innovators focus on specialized solutions, cost-effective chips, and niche applications like IoT and edge AI. Regional players leverage local manufacturing and customizations to gain an edge. Partnerships and collaborations drive growth, while barriers include R&D costs and scaling challenges.
By Regional Analysis
Region-wise, the machine learning chip market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The Asia Pacific region generated 26% revenue share in this market in 2023. This growth is driven by the rapid adoption of AI and machine learning technologies across diverse industries in China, Japan, South Korea, and India. The region has seen significant advancements in sectors such as automotive (especially with autonomous vehicles), healthcare (through AI-powered diagnostics), and telecommunications (with the rollout of 5G and edge computing).Recent Strategies Deployed in the Market
- Sep-2024: Qualcomm Incorporated unveiled the Snapdragon X Plus 8-core chip, expanding its AI PC processor range. Featuring eight CPU cores, it offers 61% faster performance with lower power consumption. The chip includes an Adreno GPU and NPU for AI tasks, promising enhanced performance, AI experiences, and improved battery life for affordable Copilot+ PCs.
- Aug-2024: Samsung Electronics Co., Ltd. unveiled new LPDDR5X DRAM chips that are 9% thinner than previous models and offer 21% better heat resistance. These chips enhance performance, particularly for AI tasks, and improve airflow in mobile devices. They support Galaxy AI applications and are also suitable for smartwatches and IoT devices, with future 6-layer and 8-layer modules planned.
- Apr-2024: Qualcomm Incorporated unveiled new industrial and embedded AI platforms alongside a micro-power Wi-Fi SoC. The QCC730 Wi-Fi solution offers significant power savings for IoT products, while the RB3 Gen 2 Platform provides high-performance processing, on-device AI, and Wi-Fi 6E support for various applications like robots, drones, and connected cameras. The platform also integrates Qualcomm's AI Hub for optimized AI models.
- Apr-2024: Infineon Technologies AG has unveiled its new PSOC Edge E8x MCU product family, designed to meet the highest certification level provided by the Platform Security Architecture (PSA) Certified program. The PSOC Edge E8x devices achieve PSA Certified Level 4 device certification by implementing an on-chip, hardware-isolated enclave for secured boot, key storage, and crypto operations. This robust embedded security certification ensures that IoT designers can develop edge applications with the highest levels of security, benefiting industries such as wearables, smart homes, printers, and payment terminals.
- Mar-2024: NXP Semiconductors N.V. teamed up with NVIDIA to integrate NVIDIA’s TAO Toolkit into NXP’s eIQ machine learning development environment. This collaboration simplifies AI model deployment on NXP’s edge devices, accelerating development with pre-trained models, transfer learning, and optimized inference, making it easier for developers to build and deploy AI solutions.
List of Key Companies Profiled
- Advanced Micro Devices Inc.
- Samsung Electronics Co., Ltd. (Samsung Group)
- NXP Semiconductors N.V.
- Qualcomm Incorporated (Qualcomm Technologies, Inc.)
- NVIDIA Corporation
- Intel Corporation
- Infineon Technologies AG
- IBM Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Cerebras Systems Inc.
Market Report Segmentation
By Technology
- System-on-Chip (SoC)
- System-in-Package
- Multi-chip Module
- Other Technology
By Chip Type
- GPU Chip
- ASIC Chip
- CPU Chip
- FPGA Chip
- Flash-Based Chip
- Neuromorphic Chip
- Others
By Industry Vertical
- IT & Telecom
- Consumer Electronics
- BFSI
- Retail
- Automotive
- Healthcare
- Media & Advertising
- Robotics Industry
- Others
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. Global Machine Learning Chip Market by Technology
Chapter 6. Global Machine Learning Chip Market by Chip Type
Chapter 7. Global Machine Learning Chip Market by Industry Vertical
Chapter 8. Global Machine Learning Chip Market by Region
Chapter 9. Company Profiles
Companies Mentioned
- Advanced Micro Devices Inc.
- Samsung Electronics Co., Ltd. (Samsung Group)
- NXP Semiconductors N.V.
- Qualcomm Incorporated (Qualcomm Technologies, Inc.)
- NVIDIA Corporation
- Intel Corporation
- Infineon Technologies AG
- IBM Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Cerebras Systems Inc.
Methodology
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