The machine learning chip market size has grown exponentially in recent years. It will grow from $28.78 billion in 2025 to $39.5 billion in 2026 at a compound annual growth rate (CAGR) of 37.2%. The growth in the historic period can be attributed to limited specialized AI hardware, low adoption of GPUs/ASICs, early-stage AI chip design, high computational cost, lack of integration standards.
The machine learning chip market size is expected to see exponential growth in the next few years. It will grow to $140.11 billion in 2030 at a compound annual growth rate (CAGR) of 37.2%. The growth in the forecast period can be attributed to growth in AI workloads, demand for edge AI devices, expansion of cloud AI infrastructure, R&D in specialized ML chips, adoption of high-performance GPUs and TPUs. Major trends in the forecast period include high-performance AI chips, edge AI hardware deployment, asic and fpga optimization for ml, neuromorphic chip development, gpu-accelerated AI computing.
The rise in cyberattacks is expected to propel the growth of the machine learning chip market going forward. A cyberattack refers to any offensive maneuver that targets computer information systems, networks, infrastructures, or personal devices with the intent to collect, disrupt, deny, degrade, or destroy information system resources or data. Machine learning chips aid cybersecurity by enabling the use of machine learning algorithms to continuously evaluate data, identify patterns, and make accurate predictions to mitigate potential cyber threats. For instance, in November 2023, according to the Anti-Phishing Working Group (APWG), a US-based international non-profit organization dedicated to cybercrime prevention, 1,624,144 phishing attacks were recorded in the first quarter of 2023, exceeding the 888,585 attacks reported in Q4 2022 and surpassing the previous record of 1,270,883 attacks in Q3 2022. Therefore, the rising number of cyberattacks is driving the growth of the machine learning chip market.
Major companies operating in the machine learning chip market are focusing on developing advanced system-on-chips (SoCs) to gain a competitive edge by integrating AI/ML acceleration, heterogeneous processing, and connectivity onto single platforms. A system-on-chip (SoC) is an integrated circuit that consolidates all essential components of a complete electronic system - including processors, memory, input/output interfaces, and specialized accelerators - onto a single chip. For instance, in May 2025, Qualcomm Incorporated, a US-based semiconductor company, launched the Snapdragon 7 Gen 4 mobile platform, which offers significantly enhanced on-device AI capabilities, including a neural processing unit (NPU) that supports generative AI assistants and large language models (LLMs) directly on the device. Delivering a 65% improvement in AI performance compared to its predecessor, this innovation highlights how mobile SoCs are evolving to incorporate powerful ML/AI accelerators, enabling richer and more efficient AI experiences on smartphones and other connected devices.
In October 2023, Advanced Micro Devices Inc., a US-based semiconductor company, made a strategic acquisition by purchasing Nod.AI for an undisclosed amount. This acquisition is expected to enhance Advanced Micro Devices Inc.'s capabilities in open AI software and reinforce its market position. Nod.ai, a US-based software company specializing in machine learning chip development, brings valuable expertise to Advanced Micro Devices Inc., aligning with the company's commitment to advancing AI technology and strengthening its overall portfolio in the semiconductor industry.
Major companies operating in the machine learning chip market are Google LLC; Samsung Electronics Co. Ltd.; Tencent Holdings Limited; Amazon Web Services Inc.; Taiwan Semiconductor Manufacturing Company Limited; Intel Corporation; International Business Machines Corporation (IBM); SoftBank Group Corp; Qualcomm Incorporated; Micron Technology Inc.; NVIDIA Corporation; Toshiba Corporation; Advanced Micro Devices Inc.; Texas Instruments Incorporated; Baidu Inc.; NXP Semiconductors; Synopsys Inc.; Lattice Semiconductor Corporation; Horizon Robotics; Hailo; Cerebras Inc.; BitMain Technologies Holding Company; Graphcore; Mythic; Gyrfalcon Technology; Flex Logix Technologies; Wave Computing, Inc.; Esperanto Technologies; BrainChip Holdings.
North America was the largest region in the machine learning chip market in 2025. The regions covered in the machine learning chip market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning chip market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Tariffs have disrupted the machine learning chip market by increasing import costs for GPUs, ASICs, FPGAs, and other high-performance hardware, particularly affecting Asia-Pacific, North America, and Europe. Enterprise and cloud AI segments are most affected. Positively, tariffs incentivize domestic chip manufacturing and drive investments in innovative, cost-efficient AI hardware solutions.
The machine learning chip market research report is one of a series of new reports that provides machine learning chip market statistics, including machine learning chip industry global market size, regional shares, competitors with a machine learning chip market share, detailed machine learning chip market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning chip industry. This machine learning chip market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
Machine-learning chips are dedicated hardware components specifically engineered to expedite the processing of machine-learning algorithms. These chips are designed to efficiently manage the intricate mathematical computations required for training and inference tasks associated with machine-learning models, outperforming general-purpose processors in these specialized tasks.
The primary types of machine learning chips include Graphics Processing Units (GPU), Application-Specific Processors (ASIC), Field-Programmable Gate Arrays (FPGA), Central Processing Units (CPU), and others. A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to accelerate computer graphics and image processing. Various technologies associated with these chips include system-on-chip (SoC), system-in-package, multi-chip modules, and others. These chips find applications across diverse industry verticals, including banking, financial services, and insurance (BFSI), IT and telecom, media and advertising, retail, healthcare, automotive, and others.
The machine learning chip market consists of sales of neural processing units (NPU), graphical processing units (GPU), control units (CU), arithmetic logic units (ALU), address generation units (AGU), and memory management units (MMU). Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 1-3 business days.
The machine learning chip market size is expected to see exponential growth in the next few years. It will grow to $140.11 billion in 2030 at a compound annual growth rate (CAGR) of 37.2%. The growth in the forecast period can be attributed to growth in AI workloads, demand for edge AI devices, expansion of cloud AI infrastructure, R&D in specialized ML chips, adoption of high-performance GPUs and TPUs. Major trends in the forecast period include high-performance AI chips, edge AI hardware deployment, asic and fpga optimization for ml, neuromorphic chip development, gpu-accelerated AI computing.
The rise in cyberattacks is expected to propel the growth of the machine learning chip market going forward. A cyberattack refers to any offensive maneuver that targets computer information systems, networks, infrastructures, or personal devices with the intent to collect, disrupt, deny, degrade, or destroy information system resources or data. Machine learning chips aid cybersecurity by enabling the use of machine learning algorithms to continuously evaluate data, identify patterns, and make accurate predictions to mitigate potential cyber threats. For instance, in November 2023, according to the Anti-Phishing Working Group (APWG), a US-based international non-profit organization dedicated to cybercrime prevention, 1,624,144 phishing attacks were recorded in the first quarter of 2023, exceeding the 888,585 attacks reported in Q4 2022 and surpassing the previous record of 1,270,883 attacks in Q3 2022. Therefore, the rising number of cyberattacks is driving the growth of the machine learning chip market.
Major companies operating in the machine learning chip market are focusing on developing advanced system-on-chips (SoCs) to gain a competitive edge by integrating AI/ML acceleration, heterogeneous processing, and connectivity onto single platforms. A system-on-chip (SoC) is an integrated circuit that consolidates all essential components of a complete electronic system - including processors, memory, input/output interfaces, and specialized accelerators - onto a single chip. For instance, in May 2025, Qualcomm Incorporated, a US-based semiconductor company, launched the Snapdragon 7 Gen 4 mobile platform, which offers significantly enhanced on-device AI capabilities, including a neural processing unit (NPU) that supports generative AI assistants and large language models (LLMs) directly on the device. Delivering a 65% improvement in AI performance compared to its predecessor, this innovation highlights how mobile SoCs are evolving to incorporate powerful ML/AI accelerators, enabling richer and more efficient AI experiences on smartphones and other connected devices.
In October 2023, Advanced Micro Devices Inc., a US-based semiconductor company, made a strategic acquisition by purchasing Nod.AI for an undisclosed amount. This acquisition is expected to enhance Advanced Micro Devices Inc.'s capabilities in open AI software and reinforce its market position. Nod.ai, a US-based software company specializing in machine learning chip development, brings valuable expertise to Advanced Micro Devices Inc., aligning with the company's commitment to advancing AI technology and strengthening its overall portfolio in the semiconductor industry.
Major companies operating in the machine learning chip market are Google LLC; Samsung Electronics Co. Ltd.; Tencent Holdings Limited; Amazon Web Services Inc.; Taiwan Semiconductor Manufacturing Company Limited; Intel Corporation; International Business Machines Corporation (IBM); SoftBank Group Corp; Qualcomm Incorporated; Micron Technology Inc.; NVIDIA Corporation; Toshiba Corporation; Advanced Micro Devices Inc.; Texas Instruments Incorporated; Baidu Inc.; NXP Semiconductors; Synopsys Inc.; Lattice Semiconductor Corporation; Horizon Robotics; Hailo; Cerebras Inc.; BitMain Technologies Holding Company; Graphcore; Mythic; Gyrfalcon Technology; Flex Logix Technologies; Wave Computing, Inc.; Esperanto Technologies; BrainChip Holdings.
North America was the largest region in the machine learning chip market in 2025. The regions covered in the machine learning chip market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning chip market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Tariffs have disrupted the machine learning chip market by increasing import costs for GPUs, ASICs, FPGAs, and other high-performance hardware, particularly affecting Asia-Pacific, North America, and Europe. Enterprise and cloud AI segments are most affected. Positively, tariffs incentivize domestic chip manufacturing and drive investments in innovative, cost-efficient AI hardware solutions.
The machine learning chip market research report is one of a series of new reports that provides machine learning chip market statistics, including machine learning chip industry global market size, regional shares, competitors with a machine learning chip market share, detailed machine learning chip market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning chip industry. This machine learning chip market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
Machine-learning chips are dedicated hardware components specifically engineered to expedite the processing of machine-learning algorithms. These chips are designed to efficiently manage the intricate mathematical computations required for training and inference tasks associated with machine-learning models, outperforming general-purpose processors in these specialized tasks.
The primary types of machine learning chips include Graphics Processing Units (GPU), Application-Specific Processors (ASIC), Field-Programmable Gate Arrays (FPGA), Central Processing Units (CPU), and others. A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to accelerate computer graphics and image processing. Various technologies associated with these chips include system-on-chip (SoC), system-in-package, multi-chip modules, and others. These chips find applications across diverse industry verticals, including banking, financial services, and insurance (BFSI), IT and telecom, media and advertising, retail, healthcare, automotive, and others.
The machine learning chip market consists of sales of neural processing units (NPU), graphical processing units (GPU), control units (CU), arithmetic logic units (ALU), address generation units (AGU), and memory management units (MMU). Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 1-3 business days.
Table of Contents
1. Executive Summary
2. Machine Learning Chip Market Characteristics
3. Machine Learning Chip Market Supply Chain Analysis
4. Global Machine Learning Chip Market Trends and Strategies
5. Machine Learning Chip Market Analysis of End Use Industries
7. Global Machine Learning Chip Strategic Analysis Framework, Current Market Size, Market Comparisons and Growth Rate Analysis
8. Global Machine Learning Chip Total Addressable Market (TAM) Analysis for the Market
9. Machine Learning Chip Market Segmentation
10. Machine Learning Chip Market, Industry Metrics by Country
11. Machine Learning Chip Market Regional and Country Analysis
12. Asia-Pacific Machine Learning Chip Market
13. China Machine Learning Chip Market
14. India Machine Learning Chip Market
15. Japan Machine Learning Chip Market
16. Australia Machine Learning Chip Market
17. Indonesia Machine Learning Chip Market
18. South Korea Machine Learning Chip Market
19. Taiwan Machine Learning Chip Market
20. South East Asia Machine Learning Chip Market
21. Western Europe Machine Learning Chip Market
22. UK Machine Learning Chip Market
23. Germany Machine Learning Chip Market
24. France Machine Learning Chip Market
25. Italy Machine Learning Chip Market
26. Spain Machine Learning Chip Market
27. Eastern Europe Machine Learning Chip Market
28. Russia Machine Learning Chip Market
29. North America Machine Learning Chip Market
30. USA Machine Learning Chip Market
31. Canada Machine Learning Chip Market
32. South America Machine Learning Chip Market
33. Brazil Machine Learning Chip Market
34. Middle East Machine Learning Chip Market
35. Africa Machine Learning Chip Market
37. Machine Learning Chip Market Competitive Landscape and Company Profiles
38. Machine Learning Chip Market Other Major and Innovative Companies
41. Machine Learning Chip Market High Potential Countries, Segments and Strategies
42. Appendix
Executive Summary
Machine Learning Chip Market Global Report 2026 provides strategists, marketers and senior management with the critical information they need to assess the market.This report focuses machine learning chip market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.
Reasons to Purchase:
- Gain a truly global perspective with the most comprehensive report available on this market covering 16 geographies.
- Assess the impact of key macro factors such as geopolitical conflicts, trade policies and tariffs, inflation and interest rate fluctuations, and evolving regulatory landscapes.
- Create regional and country strategies on the basis of local data and analysis.
- Identify growth segments for investment.
- Outperform competitors using forecast data and the drivers and trends shaping the market.
- Understand customers based on end user analysis.
- Benchmark performance against key competitors based on market share, innovation, and brand strength.
- Evaluate the total addressable market (TAM) and market attractiveness scoring to measure market potential.
- Suitable for supporting your internal and external presentations with reliable high-quality data and analysis
- Report will be updated with the latest data and delivered to you along with an Excel data sheet for easy data extraction and analysis.
- All data from the report will also be delivered in an excel dashboard format.
Description
Where is the largest and fastest growing market for machine learning chip? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The machine learning chip market global report answers all these questions and many more.The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, total addressable market (TAM), market attractiveness score (MAS), competitive landscape, market shares, company scoring matrix, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.
- The market characteristics section of the report defines and explains the market. This section also examines key products and services offered in the market, evaluates brand-level differentiation, compares product features, and highlights major innovation and product development trends.
- The supply chain analysis section provides an overview of the entire value chain, including key raw materials, resources, and supplier analysis. It also provides a list competitor at each level of the supply chain.
- The updated trends and strategies section analyses the shape of the market as it evolves and highlights emerging technology trends such as digital transformation, automation, sustainability initiatives, and AI-driven innovation. It suggests how companies can leverage these advancements to strengthen their market position and achieve competitive differentiation.
- The regulatory and investment landscape section provides an overview of the key regulatory frameworks, regularity bodies, associations, and government policies influencing the market. It also examines major investment flows, incentives, and funding trends shaping industry growth and innovation.
- The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
- The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.
- The total addressable market (TAM) analysis section defines and estimates the market potential compares it with the current market size, and provides strategic insights and growth opportunities based on this evaluation.
- The market attractiveness scoring section evaluates the market based on a quantitative scoring framework that considers growth potential, competitive dynamics, strategic fit, and risk profile. It also provides interpretive insights and strategic implications for decision-makers.
- Market segmentations break down the market into sub markets.
- The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
- Expanded geographical coverage includes Taiwan and Southeast Asia, reflecting recent supply chain realignments and manufacturing shifts in the region. This section analyzes how these markets are becoming increasingly important hubs in the global value chain.
- The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
- The company scoring matrix section evaluates and ranks leading companies based on a multi-parameter framework that includes market share or revenues, product innovation, and brand recognition.
Report Scope
Markets Covered:
1) By Chip Type: Graphics Processing Unit (GPU); Application-Specific Processor (ASIC); Field Programmable Gate Array (FPGA); Central Processing Unit (CPU); Other Chips2) By Technology: System-On-Chip (SoC); System-In-Package; Multi-Chip Module; Other Technologies
3) By Industry Vertical: Banking, Financial Services, And Insurance (BFSI); IT And Telecom; Media And Advertising; Retail; Healthcare; Automotive; Other Industry Verticals
Subsegments:
1) By Graphics Processing Unit (GPU): Dedicated GPUs; Integrated GPUs2) By Application-Specific Integrated Circuit (ASIC): Custom ASICs For AI; General-Purpose ASICs
3) By Field Programmable Gate Array (FPGA): Configurable FPGAs; Embedded FPGAs
4) By Central Processing Unit (CPU): Multi-Core CPUs; High-Performance CPUs
5) By Other Chips: Neuromorphic Chips; Digital Signal Processors (DSP); Tensor Processing Units (TPU)
Companies Mentioned: Google LLC; Samsung Electronics Co. Ltd.; Tencent Holdings Limited; Amazon Web Services Inc.; Taiwan Semiconductor Manufacturing Company Limited; Intel Corporation; International Business Machines Corporation (IBM); SoftBank Group Corp; Qualcomm Incorporated; Micron Technology Inc.; NVIDIA Corporation; Toshiba Corporation; Advanced Micro Devices Inc.; Texas Instruments Incorporated; Baidu Inc.; NXP Semiconductors; Synopsys Inc.; Lattice Semiconductor Corporation; Horizon Robotics; Hailo; Cerebras Inc.; BitMain Technologies Holding Company; Graphcore; Mythic; Gyrfalcon Technology; Flex Logix Technologies; Wave Computing, Inc.; Esperanto Technologies; BrainChip Holdings
Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Taiwan; Russia; South Korea; UK; USA; Canada; Italy; Spain
Regions: Asia-Pacific; South East Asia; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
Delivery Format: Word, PDF or Interactive Report + Excel Dashboard
Added Benefits:
- Bi-Annual Data Update
- Customisation
- Expert Consultant Support
Companies Mentioned
The companies featured in this Machine Learning Chip market report include:- Google LLC
- Samsung Electronics Co. Ltd.
- Tencent Holdings Limited
- Amazon Web Services Inc.
- Taiwan Semiconductor Manufacturing Company Limited
- Intel Corporation
- International Business Machines Corporation (IBM)
- SoftBank Group Corp
- Qualcomm Incorporated
- Micron Technology Inc.
- NVIDIA Corporation
- Toshiba Corporation
- Advanced Micro Devices Inc.
- Texas Instruments Incorporated
- Baidu Inc.
- NXP Semiconductors
- Synopsys Inc.
- Lattice Semiconductor Corporation
- Horizon Robotics
- Hailo
- Cerebras Inc.
- BitMain Technologies Holding Company
- Graphcore
- Mythic
- Gyrfalcon Technology
- Flex Logix Technologies
- Wave Computing, Inc.
- Esperanto Technologies
- BrainChip Holdings
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 250 |
| Published | February 2026 |
| Forecast Period | 2026 - 2030 |
| Estimated Market Value ( USD | $ 39.5 Billion |
| Forecasted Market Value ( USD | $ 140.11 Billion |
| Compound Annual Growth Rate | 37.2% |
| Regions Covered | Global |
| No. of Companies Mentioned | 30 |
