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Artificial Intelligence (AI) Chips Market to Grow by USD 902.65 Billion (2025-2029), Focus on AI Chips for Smartphones Drives Growth, Report with AI Trends – Technavio

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NEW YORK, Jan. 24, 2025 /PRNewswire/ — Report on how AI is redefining market landscape – The global artificial intelligence (AI) chips market size is estimated to grow by USD 902.65 billion from 2025-2029, according to Technavio. The market is estimated to grow at a CAGR of over 81.2% during the forecast period. Increased focus on developing AI chips for smartphones is driving market growth, with a trend towards convergence of AI and IoT. However, dearth of technically skilled workers for ai chips development poses a challenge. Key market players include Advanced Micro Devices Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Google LLC, Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc..

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Artificial Intelligence (AI) Chips Market Scope

Report Coverage

Details

Base year

2024

Historic period

2019 – 2023

Forecast period

2025-2029

Growth momentum & CAGR

Accelerate at a CAGR of 81.2%

Market growth 2025-2029

USD 902.65 billion

Market structure

Fragmented

YoY growth 2022-2023 (%)

61.7

Regional analysis

North America, Europe, APAC, South America, and Middle East and Africa

Performing market contribution

North America at 42%

Key countries

US, Canada, China, UK, Germany, France, Japan, Italy, India, and Brazil

Key companies profiled

Advanced Micro Devices Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Google LLC, Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc.

Market Driver

Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, automotive, and more. Deep learning and machine learning algorithms require powerful hardware components like AI chips. Advanced Micro Devices (AMD) and Nvidia lead the market with their Trainium2 chip and A100 chip, respectively. These chips use highbandwidth memory for energy efficiency and high computing power. Quantum computing and generative AI are emerging trends in AI technologies. AI chip lines, such as Ascend 910B chipset and H200 chipset, are designed specifically for AI applications. Cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud offer AI services, while edge computing enables real-time data processing on Edge devices. Ethical concerns surrounding AI use are rising. AI applications in healthcare, elder care, and IoT devices require high security and privacy. ML and DL algorithms power computer vision, image recognition, and pose detection, while cognitive computing and machine intelligence enable personalized health and treatment devices. AI technologies require various hardware components, including CPUs, GPUs, FPGAs, ASICs, DSPs, microcontrollers, frame buffers, and display devices. Energy efficiency, latency, and parallel computing are crucial factors for AI data centers. Theoretical and algorithmic basis, automatic analysis, and extraction are essential for AI applications. Patent filings for AI technologies are increasing, with applications in various industries, including manufacturing machines, wearable devices, smart homes, and connected cars. System failure and malfunctioning are concerns, and ethical considerations are necessary for the successful implementation of AI technologies. 

The Internet of Things (IoT) market is experiencing significant growth due to the numerous advantages it offers in various industries, including aerospace and defense, automotive, consumer electronics, healthcare, and more. IoT devices, which include cameras, drones, smart speakers, smartphones, smart TVs, and others, are making decisions based on data they receive without human intervention. To enhance the capabilities of these devices, manufacturers are integrating Human-Machine Interface (HMI) technologies and deploying AI chips. These chips enable power-efficient data processing and machine learning computation, allowing IoT devices to function more intelligently and autonomously. The integration of AI chips in IoT devices is a key trend driving market growth. 

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Market Challenges

Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, and automotive. However, the growing demand for AI technologies, including deep learning and machine learning, puts pressure on hardware components like AI chips. Companies like Advanced Micro Devices and Nvidia are investing in AI chip lines, such as the Trainium2 chip and Nvidia’s A100 chip, to meet this demand. These chips enable AI algorithms to run efficiently, powering applications like image recognition and pose detection. However, challenges remain. Energy efficiency is crucial, as AI data centers require vast amounts of power. Quantum computing and generative AI may offer solutions, but they present complexities. Ethical concerns around AI use also arise. Edge computing and Edge devices are becoming essential for real-time applications, reducing latency and processing data locally. Big data requires high-bandwidth memory and parallel computing, which can be achieved through system on chip, multichip module, or ASICs. Major cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud offer AI services, but they face competition from edge devices and AI data centers. AI applications in industries like healthcare, retail, finance, and automotive require specialized hardware, such as CPUs, GPUs, FPGAs, and DSPs. The future of AI lies in the intersection of AI technologies, hardware components, and ethical considerations.The AI chips market is experiencing significant growth due to the potential revenue increases for businesses adopting artificial intelligence. However, the lack of skilled labor in AI technology poses a significant challenge to market expansion. Companies must carefully consider the high research and development costs and potential talent shortage before implementing AI solutions. Enterprise AI implementation is currently hindered by the scarcity of experienced professionals with the necessary technical expertise in this field.

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Segment Overview

This artificial intelligence (ai) chips market report extensively covers market segmentation by

Product 1.1 ASICs1.2 GPUs1.3 CPUs1.4 FPGAsEnd-user 2.1 Media and advertising2.2 BFSI2.3 IT and telecommunication2.4 OthersGeography 3.1 North America3.2 Europe3.3 APAC3.4 South America3.5 Middle East and AfricaProcessing TypeApplicationTechnology

1.1 ASICs- Artificial Intelligence (AI) chips are experiencing significant growth, with application-specific integrated circuits (ASICs) leading the way. ASICs are customized chips designed for specific functions, offering faster performance than GPUs and FPGAs. Google’s Tensor Processing Unit (TPU) is an ASIC-based AI chip, specifically engineered for deep neural networks. TPU is a hardware-software solution that learns tasks by analyzing large data sets. It’s already being used in Google Search and Google Street View. Data centers are integrating TPUs into servers to manage data efficiently. TPU’s instruction set allows TensorFlow programs to be modified, enabling new algorithms. TensorFlow is an open-source machine learning library, making ASIC-based AI chips a promising choice for data center applications. The use of ASICs is driving the growth of AI chips market, providing superior performance and speed compared to GPUs, FPGAs, and CPUs.

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Research Analysis

Artificial Intelligence (AI) Chips Market: The global AI Chips Market is experiencing significant growth due to the increasing demand for advanced AI technologies in various industries. AI Chips are specialized hardware components designed to accelerate AI algorithms, including deep learning and machine learning. These chips are integral to AI applications in robotics, autonomous vehicles, healthcare, retail, finance, automotive, IoT devices, and more. Quantum computing is also expected to revolutionize AI technologies, leading to the development of more powerful AI chips. Advanced Micro and other key players are investing heavily in AI chip research and development. The market includes various types of chips such as CPU, FPGA, GPU, system on chip, multichip module, and Trainium2 chip. Ethical concerns surrounding AI are also driving the need for more efficient and specific integrated AI chips. The market is segmented into cloud and edge computing, with the edge computing segment expected to grow rapidly due to the increasing demand for real-time AI processing. Highbandwidth memory is another critical component of AI chips, enabling faster data processing and analysis. Overall, the AI Chips Market is poised for significant growth in the coming years.

Market Research Overview

Artificial Intelligence (AI) Chips Market: Overview The Artificial Intelligence (AI) Chips Market is a rapidly growing sector that focuses on designing and manufacturing specialized hardware components for AI applications. These chips are designed to accelerate AI algorithms, including deep learning and machine learning, to enable advanced functionalities such as image recognition, pose detection, behavioral patterns analysis, and natural language processing. AI Chips are essential components of various AI technologies, including robotics, quantum computing, and cognitive computing. They come in different forms, such as System on Chip (SoC), Multichip Module (MCM), CPU, GPU, FPGA, ASIC, DSP, and microcontrollers. The market for AI Chips is driven by the increasing demand for AI applications in various industries, including healthcare, retail, finance, automotive, and manufacturing. The need for real-time data processing and energy efficiency is also a significant factor driving the growth of the market. AI Chips are used in both centralized cloud servers and edge devices for data processing. Centralized cloud servers, such as Microsoft Azure, Amazon Web Services, and Google Cloud, require high computing power and high-bandwidth memory, making GPUs and CPUs popular choices for AI Chips. Edge devices, on the other hand, require low power consumption and small form factors, making FPGAs and ASICs popular choices. The market for AI Chips is also driven by the increasing use of AI in mobile applications, healthcare, and IoT devices. Ethical concerns regarding AI and the elderly population’s growing demand for personalized health solutions are also expected to fuel the market’s growth. Some of the notable AI Chips in the market include Nvidia’s A100 chip, Ascend 910B chipset, and H200 chipset. Companies are also investing heavily in patent filing and system failure prevention to ensure the reliability and efficiency of their AI Chips. The market for AI Chips is expected to continue growing as AI applications become more prevalent in various industries. The increasing use of AI in real-time applications, such as autonomous vehicles and smart homes, is also expected to drive the market’s growth. However, the market’s growth may be hindered by the high cost of developing and manufacturing AI Chips and the ethical concerns surrounding AI. In conclusion, the AI Chips Market is a dynamic and growing sector that plays a crucial role in enabling advanced AI applications across various industries. The market’s growth is driven by the increasing demand for AI applications, the need for energy efficiency, and the development of new AI technologies, such as generative AI and cognitive computing. However, the market’s growth may be hindered by ethical concerns and the high cost of developing and manufacturing AI Chips.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

ProductASICsGPUsCPUsFPGAsEnd-userMedia And AdvertisingBFSIIT And TelecommunicationOthersGeographyNorth AmericaEuropeAPACSouth AmericaMiddle East And AfricaProcessing TypeApplicationTechnology

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio’s report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio’s comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/

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CM Global Services Announces Project Santos, a Planned 50-Megawatt AI Data Center Campus in ERCOT South

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21 minutes ago

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July 23, 2026

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CM Global Services targets a site and engages with strategic partners to become operational in the AI data center space.

DENVER, July 23, 2026 /PRNewswire/ — CM Global Services, LLC (CMGS) today announced Project Santos, its plan to develop a 50-megawatt AI data center campus for a site in the ERCOT South grid zone. CMGS is a long-standing strategic partner of Compass Mining, Inc. and is a global provider of logistics, hardware sales, and infrastructure services, with a growing focus on AI infrastructure and building site development. The announcement was made by Shanon Squires, Chief Mining Officer of Compass Mining, during a panel on bitcoin mining companies diversifying into AI infrastructure at the Energy Investors Forum.

CMGS intends to deliver Project Santos in two phases. The first phase, a 7-megawatt, 5 MW of IT Load Tier III facility purpose-built for AI inference workloads, is targeted for completion by the end of the first quarter of 2027. A subsequent 43-megawatt expansion, bringing the site to its fully planned 50-megawatt capacity

“This is a disciplined next step for CM Global Services, drawing upon its expertise in standing up infrastructure, while Compass Mining simultaneously continues to be the gold standard in Bitcoin mining-related services,” said Shanon Squires. “Bitcoin mining remains the core of Compass Mining. CMGS’ Project Santos reflects the power infrastructure and site development discipline CMGS built over years, and we’re pursuing this initiative on our own terms.”

“This is a new step forward for CMGS, as we continue building for the future,” said Vishnu Mackenchery, Managing Director at CMGS. “Project Santos marks our entry into AI infrastructure and inference, and we’re charting our own path, moving fast to get there.”

GPU-as-a-Service for Enterprise and Neocloud Customers

Project Santos is being developed as a GPU-as-a-Service (GPUaaS) platform. Rather than requiring customers to bring their own hardware, CMGS is securing NVIDIA GB300 Blackwell GPU capacity to offer directly to off-takers as dedicated, single-tenant or multi-tenant compute. The company’s ideal customer profile is AI enterprise organizations seeking dedicated capacity, and CMGS is also in active discussions with neocloud providers.

Project Status

Site: located in the ERCOT South grid zoneCompute: CMGS is securing NVIDIA GB300 Blackwell GPU capacity to offer as GPU-as-a-Service to off-takersTotal planned capacity: 50 megawatts, 35 MW of IT to be delivered in two phasesPhase 1: 7 megawatts, 5 MW of IT load Tier III, targeted for completion by end of Q1Phase 2: adding a 43-megawatt expansion, 30 MW of IT load with utility-supported expansionCustomer profile: AI enterprise companies are the ideal customer; CMGS is also in active discussions with neocloud providers

About CMGS

CM Global Services (CMGS) is a global provider of logistics, hardware sales, and infrastructure services, with a growing focus on AI infrastructure and building site development. CMGS supports clients with end-to-end logistics solutions, hardware procurement, and site-level execution for next-generation compute infrastructure.

About CMGS and Compass Mining Partnership

Compass Mining serves as a strategic partner and advisor to CM Global Services (CMGS), supporting its growth across global logistics, hardware sales, and infrastructure services. As CMGS expands its focus into AI infrastructure and site development, Compass Mining’s guidance helps shape its strategic direction and execution. Together, the two organizations continue to collaborate on delivering end-to-end solutions for clients building next-generation compute infrastructure.

Disclaimer

This communication contains forward-looking statements relating to a potential closing of a transaction. There can be no assurance that the proposed transaction will be completed on the terms described, or at all. Forward-looking statements are subject to significant business, economic, and competitive uncertainties, many of which are beyond our control. This communication is for informational purposes only and does not constitute an offer to sell, or a solicitation of an offer to buy, any securities of the company. Furthermore, investing in or engaging with our company involves substantial risk, and past performance or previous communications are not indicative of future results. There is no guarantee, assurance, or warranty that any specific financial outcome, return on investment, or overall results will be achieved. Actual results may differ materially and adversely from those expressed, projected, or implied in any forward-looking statements. Investors and stakeholders should not rely solely on preliminary press releases regarding potential transactions or projected financial metrics when making investment decisions. We undertake no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise, except as required by applicable securities laws. Prospective investors are strongly encouraged to conduct their own independent due diligence and consult with a qualified, independent financial or legal advisor prior to making any investment.

Contact
All inquiries can be made to: Santos@CMGlobalServices.io 

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Advantech Unveils Next-Gen AI Infrastructure Solutions Powered by AMD EPYC™ 9006 Series Processors

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July 23, 2026

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TAIPEI, July 23, 2026 /PRNewswire/ — Advantech, a global leader in industrial edge computing and edge AI solutions, today announced its next-generation server and network platforms powered by the latest AMD EPYC™ 9006 Series processors. Designed to accelerate AI infrastructure from the data center to the intelligent edge, Advantech’s 6th Gen AMD EPYC-powered servers deliver the performance, scalability, and reliability organizations need for AI, HPC, storage, networking, and mission-critical industrial workloads.

At AMD Advancing AI 2026, Advantech will showcase its latest 2U 4-node edge server and EATX server board, demonstrating how its workload-ready server solutions enable customers to build scalable, high-performance AI and edge computing infrastructure with greater deployment confidence.

Continuing Performance Leadership with AMD EPYC 9006 Series Processors

6th Gen AMD EPYC server CPUs bring continued leadership in performance, efficiency, memory bandwidth, and next-generation I/O. Featuring up to 128 cores and 256 threads, advanced 2nm process technology, “Zen 6” and “Zen 6c” architecture, up to 20% average generational performance uplift, and up to 20% performance-per-watt improvement, AMD EPYC 9006 Series processors are designed to support more virtual machines, higher throughput, and better system efficiency. With up to 128 PCIe Gen6 lanes per CPU, CXL™ 3.1 memory expansion, and support for DDR5 8000NHz and MRDIMM 12800MHz for high memory bandwidth, Advantech edge server solutions deliver balanced compute, memory, and I/O performance for next-generation AI, telco, edge, and storage infrastructure.

Key Features Include:

Up to 128 cores / 256 threads with “Zen 6” and “Zen 6c” architectureAdvanced 2nm process technology for improved performance and efficiencyUp to 20% average generational performance uplift and 20% performance-per-watt improvementDDR5-8000 and MRDIMM 12.8G support for higher memory bandwidth and capacityPCIe® Gen6 scalability: up to 128 lanes for 1 CPU and up to 196 lanes for 2 CPUsCXL™ 3.1 support for optimized memory expansion

Comprehensive Edge Server Solutions from Edge to Cloud

Advantech’s edge server portfolio powered by AMD EPYC™ 9006 Series processors delivers a complete board-to-system lineup for AI infrastructure, data centers, cloud, HCI, HPC, edge computing, industrial applications, and high-performance networking. The first-wave portfolio includes:
(1) The SKY-642E5, 4U MGX GPU server, for large-scale AI acceleration
(2) The SKY-722E5, 2U DC-MHS server with DC-SCM support, for modular data center and edge AI deployments
(3) The SKY-712E5, 1U DC-MHS server, supporting HHHL and FH-3/4L expansion cards for high-density enterprise edge and cloud workloads
(4) The SKY-822E5, 2U short-depth DC-SCM modular server, supporting 2–3 dual-slot GPU cards for space-constrained edge data centers
(5) The SKY-924E5F, 2U 4-node front-access server, for distributed edge computing,
(6) The ASMB-982 & ASMB-832 server boards for flexible, high-expandability system designs.

These new platforms also support PCIe Gen6 scalability, GPU-optimized architecture, advanced DDR5/MRDIMM memory, and AFA-ready high-density E1.S/E3.S NVMe SSD storage to meet low-latency data access, high-throughput storage performance, and scalable infrastructure for data-intensive AI and edge-cloud workloads.

Expanding the portfolio further, Advantech also introduces the FWA-6084, the 2U network appliance and is designed for demanding network security and edge AI workloads. It features DDR5/MRDIMM memory capability, eight Gen6 network module cards, and one PCIe Gen5 x16 slot for GPU or add-on card expansion. It is well positioned to support line-speed multiple 200G network workloads without compromise.

Together with Advantech’s unique service advantages—including 3-5-10 service guarantee, strict revision control, stable component supply, worldwide local support, and custom-ready integration—the new portfolio supports customers reduce deployment risk, secure long-term product roadmaps, and accelerate workload-ready AI and edge-cloud infrastructure from concept to deployment.

Explore more product information, please contact us or visit the Advantech x AMD website.

About Advantech

Advantech is a global leader in IoT intelligent systems and embedded platforms, driven by its vision of “Enabling an Intelligent Planet.” To address the growth of edge computing and AI, Advantech focuses on five key markets: Edge Intelligence Systems, Manufacturing, Energy and Utilities, iHealthcare, and iCity Services & iRetail. By integrating edge computing hardware, WISE-IoT software, sector-specific AI solutions, and domain expertise, Advantech creates an orchestration model that connects industrial ecosystems and accelerates industrial intelligence with partners and customers.(www.advantech.com) 

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SOURCE Advantech Co., Ltd.

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MulticoreWare and AMD Collaborate to Advance Physical AI and Autonomous Robotics on AMD Platforms

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July 23, 2026

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Companies Demonstrated Real-Time Multimodal AI and Vision-Language-Action Workflows on AMD Ryzen™ AI Platforms at AMD Advancing AI 2026

SAN JOSE, Calif., July 23, 2026 /PRNewswire/ — MulticoreWare, Inc., a global technology company specializing in AI software solutions, physical AI, accelerated computing, and engineering services, today announced its ongoing collaboration with AMD to advance autonomous robotics and edge intelligence on AMD platforms.

As part of this collaboration, MulticoreWare joined AMD at AMD Advancing AI 2026 to present ‘Enabling Physical AI on AMD’, demonstrating how advanced vision, language, and action (VLA) models can drive real-time robotic intelligence on AMD Ryzen™ AI Embedded platforms.

As AI increasingly moves from the cloud into robots, autonomous systems, and intelligent edge devices, organizations need efficient ways to run sophisticated AI models closer to where decisions need to be made. Together, AMD and MulticoreWare are helping developers bring advanced perception, reasoning, and action capabilities to AMD-powered systems.

At AMD Advancing AI 2026, AMD and MulticoreWare demonstrated how multimodal VLA models run on AMD Ryzen™ AI Embedded integrated GPUs using AMD ROCm™, enabling robots to perceive, reason, and act in real time. The session showcased practical guidance for AI developers, robotics engineers, and innovators building next-generation intelligent machines on AMD Embedded platforms.

“Physical AI is reshaping how machines perceive, decide and act in the real world,” said Sumit Shah, Head of Product Management and Marketing, Adaptive and Embedded Computing Group, AMD. “AMD Ryzen™ AI Embedded X100 Series processors deliver a scalable, open x86 Embedded platform that unifies AI, real-time control and industrial reliability to enable the generation of autonomous systems without locking developers into a single compute architecture or software stack.”

“A Physical AI system depends on a tightly integrated loop between perception and actuation. It must operate in real time, on real hardware, and in environments that are inherently unpredictable,” said Vish Rajalingam, VP & GM, Mobility and Transportation BU at MulticoreWare. “That makes it a hardware-software co-design challenge, not simply an AI inference problem. Building on the open-source AMD Robotics Software Suite, we work closely with OEMs to optimize the entire stack so that latency, reliability and accuracy targets are consistently achieved in production environments. That’s the integration MulticoreWare and AMD deliver together to move intelligent robotic systems from prototype to deployment.”

This session builds on more than 15 years of collaboration, with MulticoreWare delivering software optimization, AI, and engineering expertise across the AMD ecosystem, including Ryzen™ AI, Ryzen™, AMD EPYC™, AMD Instinct™, AMD Radeon™, and adaptive computing technologies.

About MulticoreWare

MulticoreWare, Inc. is a global technology company delivering AI software solutions and engineering services that accelerate innovation in Physical AI, Agentic AI, Robotics, Edge Intelligence, and Accelerated Computing. With expertise in multimodal AI, Vision-Language-Action (VLA) models, sensor perception and fusion, AI optimization, embedded systems, and high-performance software, MulticoreWare helps customers transform advanced AI technologies into production-ready solutions. Its innovations power applications across automotive, robotics, industrial automation, smart cities, healthcare, defense, and intelligent edge devices, while its video codec technologies enable next-generation video experiences worldwide.
www.multicorewareinc.com

AMD, the AMD Arrow logo, EPYC, Instinct, Radeon, Ryzen and combinations thereof are trademarks of Advanced Micro Devices, Inc.

Contact:
Suchithra Thyagarajan
VP – Corporate Marketing
marcom@multicorewareinc.com 

 

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