AI Hardware Market Size - By Processor, By Memory & Storage, By Application, By Deployment, Growth Forecast, 2025 - 2034

AI Hardware Market Size

The global AI Hardware market size was estimated at USD 59.3 billion in 2024. The market is expected to grow from USD 66.8 billion in 2025 to USD 296.3 billion in 2034, at a CAGR of 18%.

• The growth of applications such as ChatGPT and DALL·E have increased the demand for specialized AI hardware at a previously unanticipated rate. These applications and others like them require high levels of computational throughput which is driving investment in GPUs, TPUs, Brainded AI and even ASICs. This further stimulates the sale of AI chips, data centers, and the creation of hardware that is designed to meet the needs of next-gen AI applications.
   
• AI is being used by enterprises and OEMs at the edge on industrial-grade IoT devices, smartphones, and even at the endpoints to isolate areas of lag and enable real-time decision-making. For instance, Qualcomm in October 2023 introduced the Snapdragon 8 Gen 3 platform which features an AI engine capable of on device processing of large language models Llama 2 and Whisper with no cloud burden.
   
• In 2019, the AI hardware sector was emerging due to the need for GPUs by data center facilities and scholarly work. The data center segment of NVIDIA which includes AI GPUs like the Tesla V100 generated revenues of USD 2.98 billion in 2019 as compared to USD 1.93 billion in 2018.
   
• The COVID-19 pandemic witnessed that there was an integrated shift in enterprise infrastructure strategy due to accelerations in AI integrations and cloud migration. This resulted in rapidly evolving requirements for data centers and edge environments which in turn increased the need for memory and chipsets optimized for AI. Nearly all hardware deployments were AI-ready by 2024 due to investments from hyperscale participants and the expansion of ecosystem collaborations.
   
• The use of artificial intelligence is transforming areas such as diagnostics, imaging, and genomics along with the pharmaceutical sector. These areas demand sophisticated hardware with high levels of processing and storage. For instance, in March 2025, Subtle Medical will utilize NVIDIA’s newest GPUs and DGX systems to GenAI technology, greatly improving medical imaging. Currently, their ultra-low-dose MRI, CT, and PET scans have reduced radiation exposure by 75%, increased scan speeds five-fold, and enhanced lesion visibility.
   
• North America leads the AI hardware market, Open AI and Microsoft both announced a multi-phase AI supercomputing project for US campuses which is planned to be completed in 3 phases. With Phase 3 active and a planned “Stargate” buildout of 100 billion dollars by 2028. This collaboration includes Microsoft funded data centers with NVIDIA GB200 “Blackwell” AI chips installed which will be used for LLM training further solidifying North America’s dominion over the computation infrastructure and AI regional supremacy.
   
• Asia-Pacific is the fastest-growing region, driven by AI national policy programs, self-sufficient semiconductor programs, and growing edge computing requirements, China, India, and South Korea are investing in the design and production of AI chips. As an example, India approved its India AI Mission in 2024 which boosts the country’s spending by approximately USD 1.24 billion over five years toward semiconductor infrastructure and enhancing the country’s digital economy ambitions.

AI Hardware Market Trends

• The shift from general-purpose GPUs to NPUs and Asics designed for particular functions such as NLP, image recognition, and training, is redefining AI hardware tactics. The trend began in 2021 with Google’s TPUs, Amazon’s Trainium/Inferentia and Apple’s Neural Engines, all motivated by the need for high-performance, power-efficient, and vendor-flexible alternatives. It allows for better integration between software and hardware, boosting system security. Most industry tech leaders are anticipated to shift towards a proprietary silicon ecosystem by 2026, making it the industry standard.
   
• The attempt towards achieving silicon independence, companies are starting to face development holdups and scalability issues which is now making the complexity of custom AI accelerator design more apparent. This was exemplified in June 2025 when Microsoft pushed back the production of its custom “Maia” AI chip by six months. This trend began in late 2023, as hyperscale’s aimed to reduce reliance on NVIDIA. Though there are facing design setbacks, this initiative is anticipating operational traction by 2027 which would enable a diversified chip supply chain and task-specific processing throughout cloud and edge.
   
• The existing and growing need for smaller circuits, like energy-efficient chips, that can process data where it is collected, with real-time decision-making capabilities. Such need was supported by Qualcomm, NVIDIA, and Intel. Further, autonomous vehicles, drones, and industrial IoT applications fueled it in early 2022. Tasks such as data processing at the edge improve privacy, lower latency, and are vital to sectors like healthcare and manufacturing. As such, the trend is poised to gain pace in emerging markets by 2026, as edge infrastructure proliferates in low-bandwidth environments.
   
• The integration of high-bandwidth memory (HBM) in AI hardware is emerging as a key thing for handling large-scale AI model training and inference. This trend began in mid-2022, as chipmakers like SK Hynix, Samsung, and Micron accelerated development of HBM3 and next-gen memory architectures to support GPUs and AI accelerators. Driven by the memory-intensive demands of generative AI and LLMs, this trend enhances processing speed, reduces bottlenecks, and supports parallelism. It is expected to become mainstream by 2025, powering advanced AI workloads across cloud, HPC, and edge data centers.
   

AI Hardware Market Analysis

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Based on processor, the AI hardware market is segmented into graphics processing units, central processing units, tensor processing units, application-specific integrated circuits, field-programmable gate arrays, neural processing units. The graphics processing units segment held a market share of around 39% in 2024 and is expected to grow at a CAGR of over 18% from 2025 to 2034.
 

• The advancements in AI technologies, particularly in AI-driven automation systems, are extensive and gaining popularity. Given their unparalleled prowess in parallel processing, computing, memory bandwidth, and the training and inference of large-scale models, GPUs are more than industry leaders; they dominate the market of artificial intelligence cloud hardware in both enterprise and research sectors.
   
• Neural processing units are growing at a CAGR of over 19%, due to surge in the need for on-device AI and energy-efficient inference processes is the primary factor driving this change. Adoption is currently limited by integration complexity and vendor-specific architectures. Provided that contemporary AI hardware platforms increasingly integrate NPUs with CPUs and GPUs for real-time AI execution on edge devices, mobile, automotive, and IoT ecosystems benefit from low-latency, power-efficient applications that do not require cloud resources.
   
• The rise of AI-driven applications in enterprises, there is more focus being placed on real-time optimizations for GPU efficiency. This is leading to the creation of inference-optimized GPUs made for deployment within edge servers, autonomous systems, and smart devices, featuring reduced size and power consumption.
   
• For instance, in March 2024, NVIDIA has released the L4 GPU, which Google Cloud has now integrated into its Vertex AI. NVIDIA released L4 in March 2024, claiming that it was able to perform video and AI inference workloads 120× better than edge CPUs and the analogs of edge AI inference were. The growing shift towards GPUs programmed specifically for real-time AI inference has reached a new height.
   
• The total market value of AI hardware stands at approximately 68 % with the inclusion of application-specific integrated circuits (ASICs), neural processing units (NPUs), and graphics processing units (GPUs) showing moderate concentrate. These segments enhance execution of training large language models as well as real-time inference on edge devices, outperforming general-purpose CPUs alongside phasing out outdated architectures in performance-centric situations.
   

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Based on memory & storage, the AI hardware market is segmented into high bandwidth memory, AI-optimized dram, non-volatile memory, emerging memory technologies. The high bandwidth memory segment held a market share of 47% in 2024, and the segment is expected to grow at a CAGR of over 19% from 2025 to 2034.
 

• The huge demand for parallel data processing with minimal latency in advanced AI workloads continues to increase the need for hardware bandwidth memory (HBM). Considering the growing number of use cases for large language models and generative AI, HBM helps with necessary speed and capacity requirements for both training and inference.
   
• There are AI models with HBM have the capability for near instantaneous retrieval of stored data which increases responsiveness in real-time systems with no lags. This greatly affects the uptake of enterprise infrastructure. 
   
• For instance, in July 2025, Micron will launch the HBM4 36 GB chip with a height of 12 layers, aimed at AI data centres. The new level of integration HBM's implementation in advanced AI accelerators. The new HBM variants are designed to address bandwidth constrained memory bottlenecks of advanced AI workloads.
   
• AI-Optimized DRAM expands at a CAGR exceeding 18%, its adoption is projected to increase significantly due to its capacity to allow quick data exchange during training processes. Industry leaders such as Samsung and SK Hynix, which supply advanced low latency and high-speed DRAM to AI accelerators and GPUs, further shore up this market.
   
• Non-Volatile Memory is growing at a CAGR of 15% because of their ability to retain data without power. For instance, Micron and Intel are pursuing breakthroughs in this area, considering NVM as crucial for persistent storage needed during AI inference and real-time decisions in data-driven AI systems and real-time decision-making under power-constrained environments.
   

Based on application, the AI hardware market is segmented into data center and cloud computing, automotive and transportation, healthcare and life sciences, consumer electronics, industrial and manufacturing, financial services and telecommunications. The data center and cloud computing segment are expected to grow, driven by the escalating demand for large-scale AI model training, high-performance computing, and scalable infrastructure to support generative AI workloads.
 

• The data center and cloud computing segment dominate the artificial intelligence hardware market with organization building their new data centers tailored to specific workloads. These purpose-built AI facilities include GPUs, TPUs, and proprietary AI accelerators. Microsoft, Amazon, Google, and other industry leaders allocating considerable funds are directing new infrastructural development to accommodate large-scale, requisitive AI-driven workloads.
   
• For instance,in June 2025, with its second generation Trainium 2 chips, Amazon's project rainier marks an investment of $100 billion into purpose-built AI data center cluster s. It seeks to support large language model training for clients like Anthropic and features hundreds of thousands of bespoke AI processors which moves subsequent steps toward vertically integrated infrastructure at hyperscale and further optimization for AI.
   
• The integration of advanced driver-assistance systems (ADAS), autonomous vehicles, and real-time sensor fusion are transforming the automotive and transportation industries AI on a propelling AI technology to automakers and account for approximately 16% of the AI hardware market share.
   
• AI hardware adoption in consumer electronics is surging with a CAGR of around 18%, the proliferation of smartphones, smart speakers and augmented/virtual reality headsets emphasizes the importance of on-device intelligence. The capabilities of NPUs and AI-centric processors are making real-time imaging, translation, and personalization feasible. As edge AI becomes more prevalent, the consumer electronics sector remains a hotbed for the aggressive commercialization of miniature and energy-efficient AI processors.
   
• The organizations are engaged in manufacturing and industrial activities are applying AI technologies for predictive maintenance, robotic automation, as well as quality control. AI-based vision systems coupled with edge inference devices are optimizing productivity and minimizing downtime on the shop floor. Growth is driven by the adoption of smart factories, Industry 4.0, and an increasing demand for real-time ruggedized AI hardware built for harsh industrial environments.
   

Based on deployment, the AI hardware market is segmented into cloud-based AI hardware and on-premises ai infrastructure. The cloud-based AI hardware segment is expected to grow, due to its ability to deliver flexible, secure, and cost-efficient AI infrastructure.
 

• The cloud-based AI hardware segment dominates the artificial intelligence (AI) hardware market. The integration of AI chips like Googles TPUs, AWS Trainium logics and Microsoft Athena have made AI empower large scale training. The provision of AI compute is increasingly becoming cost effective and rapid due to streamlined processes built over the years.
   
• For instance, in May 2024, Google Cloud’s TPU v5e is identified as the sole provider of scalable GenAI workloads and is aiding enterprise clients in saving 50% of the cost on training.
   
• It is forecasted that by the year 2024, infrastructure in the cloud would have advanced enough to handle the immense workloads needed for hyper ecient AI generation tools like Chat GPT, Bard and Claude. Businesses would be able to reduce spending on infrastructure with sophisticated, scalable software and sophisticated generative AIs utilizing the cloud.
   
• For instance, in March 2025, AWS recently released G6e EC2 instances with NVIDIA L40S GPUs. These are aimed at local deployment of LLMs and cloud production of LLM-generated images, audio and video content. Such LLMs are tailored for generative AI applications, showcasing the direction in which the cloud sector intends to pivot—toward more customized infrastructure to support the ongoing advancements in artificial intelligence technology.
   
• On-premises AI infrastructure contributes around 32% of the AI hardware market share with a CAGR of around 15%. The trend is particularly useful for businesses managing critical data that necessitate low-latency processing. It enables more control as well as data privacy and customization for AI workloads in industries like healthcare, defense, and finance. It also reinforces hybrid AI systems and will likely expand in parallel with the use of edge and private cloud infrastructures.
   

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US dominated the AI hardware market in North America with around 91% market share and generated revenue of USD 19.8 billion in 2024.

• US maintained a substantial portion of the AI hardware market, owing to unparalleled prowess in innovation, supply chain, and infrastructure, further making the country's leadership on AI foundational technology.
   
• The leading American companies such as NVIDIA, AMD, Intel and Qualcomm are primary manufacturers of AI hardware. They have a commanding market share on the industry’s GPUs, AI accelerators, and custom chips. U.S. companies continued to launch advanced processors designed for training large language models and real-time inference in 2024.
   
• Canada is growing in the AI hardware market with a CAGR of 22 % due to the national AI policies, government sponsored R&D programs, and active collaborations of the university and the industry. The healthcare and energy sectors concern edge AI, and there is a need for data privacy focused hardware which is low power consuming. Canada is also increasingly becoming an infrastructure growth hub for AI owing to initiatives such as the Pan-Canadian AI Strategy.
   
• The AI adoption in healthcare, finance, retail, automotive, and manufacturing surged in the U.S. in 2024. This created new challenges in data analysis, automation, and computation. 
   
• For instance, in January 2025, NVIDIA partnered with GE healthcare in January 2025 to deploy Blackwell GPU-powered AI imaging systems, enhancing diagnostics while also cutting costs, demonstrating how vital computing drives advanced demand in crucial sectors.
   

The AI hardware market in Europe is expected to experience significant and promising growth from 2025 to 2034.
 

• Europe holds the third-largest portion of the AI hardware market with an annual growth rate of 17.2%, this is being fueled by the need for sovereign AI infrastructure, data localization, and sectorial AI implementations. EU-sponsored policies like the IPCEI on semiconductors as well as Horizon Europe are spearheading the utilization of AI chips across the multifunctional ecospheres, including public and industrial domains like automotive and healthcare.
   
• The AI semiconductor hardware industry in Germany has a leading position in Europe, driven by the country’s strategy on Industrial Revolution 4.0 and bolstered by government investments in AI-powered semiconductors. Apart from the German Future Fund committing more than €1.6 billion on AI and semiconductor technologies, other microprocessors allocated spending included advanced automotive multilayer microprocessors, robotics, industrial automation, and other sectors.
   
• The UK is emerging as a focal point of new innovations in semiconductor design fueled by its £1 billion national AI strategy and investment into other AI research centers like the Bristol & bath semiconductor hub. The UK is focusing on sovereign computing infrastructure as well as quantum-AI which creates demand for high performance processors and memory chips in finance, defense, and life sciences.
   
• Italy is expanding its AI hardware capabilities, fueled by EU recovery funds and initiatives such as the National Plan for Digital Transition. With growing interest in AI applications in smart manufacturing, automotive, and public administration, Italy is increasing adoption of edge AI devices and investing in regional semiconductor R&D to boost domestic innovation.
   

The AI hardware market in China is expected to experience significant and promising growth from 2025 to 2034.
 

• Asia Pacific accounts for over 24% of the AI hardware market in 2024 and is the fastest-growing region with a CAGR of around 20%. Growth is fueled by aggressive national AI strategies, rising demand for edge computing, and major investments in semiconductor self-reliance.
   
• India is strategically positioning itself as an AI hardware center in Asia due to the India AI Mission (2024) and new semiconductor subsidy policies. India now has earmarked over $1.24 billion on its AI infrastructure and pretends to build domestic chip design, foster public-private R&D coalitions, and subsidize AI at the edge in healthcare, agriculture, and fintech.
   
• Vietnam is gaining momentum in the AI hardware landscape, supported by national digital transformation goals and partnerships with global semiconductor firms. Investments in AI R&D zones and smart city initiatives are accelerating adoption of edge AI chips for public safety, traffic management, and industrial IoT, making Vietnam a fast-growing base for AI-enabled infrastructure.
   
• AI hardware markets of China and Japan have different purpose. China is boosting investment in infrastructure and manufacturing AI chips, on the other side Japan emphasizes robotics and edge AI for elder care and industrial automation. Both nations put a premium on autonomous systems and real-time analytics, requiring secure and high-performance AI hardware.
   
• Emerging markets such as Indonesia, Vietnam, and the Philippines are fueling regional growth in the AI hardware market, driven by rising smartphone penetration, government-backed digitalization, and increasing edge AI deployment in healthcare, agriculture, and logistics. Hardware solutions that are energy-efficient, cost-effective, and adaptable to low-connectivity environments are well-positioned to address infrastructure gaps in these high-potential, underserved economies.
   

The AI hardware market in Brazil is expected to experience significant and promising growth from 2025 to 2034.
 

• Latin America is growing at a CAGR of 15.6% in the AI hardware market, driven by expanding urban digitization, smart city projects, and the adoption of AI in public services and manufacturing.
   
• Brazil is allocating resources to balance infrastructure with expansion of cloud services and the construction of new data centers. The strategic global and local cloud players are establishing facilities in São Paulo owing to heightened enterprise demand for AI-calculating capabilities. These facilities demand cutting-edge GPUs, TPUs, and accelerators for machine learning, analytics, and AI-as-a-service offerings.
   
• Mexico and Colombia are among the most active adopters of AI hardware in Latin America, driven by smart city projects, industrial automation, and public sector digitization. Colombia has advancements in healthcare and logistics AI are supported by public-private partnerships and government initiatives, while Mexico focuses on emerging trends in surveillance and mobility AI due to its proximity to U.S. chip fabs.
   
• Argentina, Chile and Peru serve as new stops which integrate the adoption of AI hardware spurred by university initiatives in sophisticated regional agriculture and infrastructure development. Argentina's academic prowess assists the miniaturization of AI devices. The Chilean mining and renewables sectors are integrating edge AI chips. Peru concentrates on low power mobile-first hardware for the remote and underserved regions.
   

The AI hardware market in Saudi Arabia is expected to experience significant and promising growth from 2025 to 2034.
 

• MEA region made up 7% of the world's AI hardware market in 2024. This is indicative of constant progress brought about by national AI policies, smart city initiatives, and ‘intelligent infrastructure’ projects in the Gulf countries. With AI chips and hyper-scale data centers from the UAE and Saudi Arabia furthering the MEA AI infrastructure market, some parts of Africa still struggle with outdated infrastructure that slows down the adoption of AI hardware on a greater scale.
   
• The United Arab Emirates is at the forefront of the AI hardware industry within the MEA region owing to its artificial intelligence strategy 2031, sovereign computer infrastructure spending, and Dubai’s smart city initiatives. The UAE seeks to deploy AI-optimized chip technology in healthcare, energy, and public services which would make the country a focal point for cutting-edge AI infrastructure and innovation in the region.
   
• The opportunity is emerging in Nigeria, Kenya, and Egypt as they pursue AI hardware development stemming from their national digital agendas and supported by the entrepreneurial environment. Moreover, these countries’ interests also encompass low-power edge devices and localized computing infrastructure for agriculture, education, and healthcare.
   
• The strategic openings are emerging in Nigeria, Kenya and Egypt as they establish AI hardware development, driven by national digital agendas and bolstered by the entrepreneurial climate. Additionally, the focus of these countries also includes low-power edge devices and localized computing infrastructure in the fields of agriculture, education, and healthcare.
   
• For instance, in Kenya, Apollo Agriculture employs satellite images and edge low-power artificial intelligence technology to offer precision farming guidance for smallholder farmers, thereby increasing crop yields through real-time advisories on planting and scheduled care for the crops.
   

AI Hardware Market Share

The top 7 companies of the AI hardware industry are NVIDIA, Microsoft, Qualcomm Technologies, Amazon Web Services (AWS), Intel, Advanced Micro Devices, Apple around 83% of the market in 2024.
 

• NVIDIA was the first to offer GPUs, which are now critical in AI training for the data centers and edge computing. Its CUDA platform together with the DGX systems are instrumental in machine learning, deep learning, and generative AI. NVIDIA is now also into AI infrastructure and networking software. With almost 50% of the global AI chip market, NVIDIA is integral to LLMs and AI infrastructure worldwide.
   
• Microsoft invests heavily in AI optimization, especially since the launch of their cloud platform Azure, with NVIDIA GPUs integration, as well as customized AI chips like Azure Maia. With Open AI, Microsoft is planning next generation AI features for Microsoft 365 and Copilot, a universal assistant.
   
• Qualcomm stands out as one of the foremost suppliers of AI hardware for edge and mobile devices. Its snapdragon platforms incorporate AI into smartphones, wearables, and automotive technology through neural processing units. Qualcomm AI Engine enables on-device inference for vision, speech, and predictive tasks using AI technologies. The company is further developing AI solutions for IoT and robotics.
   
• As one of the leading providers of AI infrastructure on cloud, AWS offers AI chips of its own design, Trainium and Inferentia, for model training and inference respectively. They are used in AWS data centers to support Sage Maker, Bedrock, and other generative AI tasks. AWS also supports NVIDIA and AMD GPUs. To remain a foundational cloud AI provider, AWS is strengthening its position by increasing its global infrastructure and hardware offerings to keep up with the need for AI in the cloud and innovation by enterprises and start-ups.
   
• Intel has been one of the industry's top players in computing hardware. They not only offer AI-optimized products like the Xeon processors, Habana Labs Gaudi AI accelerators and FPGAs, but also focus on cloud and edge AI solutions in healthcare, industrial automation, and data centers. The company places a strong emphasis on software stacks for AI as well as on open platforms like Open VINO. With more continued R&D in neuromorphic computing and scalable AI chips, Intel hopes to avert other competition by providing integrated solutions for inference and training across different sectors.
   
• AMD provides one of the Instinct series high-performance GPUs for data centers and AI workloads. They do have competitors, but with their MI300 and soon-to-be-released MI350 chips, they will be able to squash NVIDIA’s grasp on training large models. AMD is also moving into full-rack AI systems and buying companies like Pensando and Eno semi to further develop AI as well as silicon photonics.
   
• Apple implements AI capabilities with its in-house crafted silicon, such as the Neural Engine embedded in A-series and M-series chips. These devices facilitate the functioning of Face ID, Siri, photo enhancement, and machine learning algorithms operating on the device. Apple achieves high privacy standards and performance by using local AI processing.
   

AI Hardware Market Companies

Major players operating in the AI hardware industry are:

• Advanced Micro Devices (AMD)
• Amazon Web Services (AWS)
• Apple
• Google
• IBM
• Intel
• Microsoft
• NVIDIA
• Qualcomm Technologies
• Samsung Electronics
   
• AMD has launched a new set of accelerators and full-rack systems named Instinct MI350 which focuses on high-performance AI computing within data centers. NVIDIA has also introduced powerful Blackwell GPU architecture, unveiling project GROOT, latest AI personal computing technology. AWS have introduced new chips with Trainium and Inferentia, enabling advanced, cost-effective scaling for training and inference within cloud AI models. 
   
• Intel have also released new Gaudi 3 AI chips and processors, increasing efficiency of ai powered systems within enterprises. Google on the other hand, has issued its own Axion CPU and developed TPU v5p (Ironwood) to improve internal AI workload. Microsoft issued ai-powered devices with azure Maia 100 chips and copilot PCs embedded with high-power NPUs for fluid AI experience. 
   
• Baltra, a custom server chip is under development for apple’s internal AI while Qualcomm released the Snapdragon X Elite and Cloud AI 100 Ultra processors maintaining the industry benchmark for AI efficiency per watt. IBM enhanced its Telum II AI processors while preparing North Pole chips aimed at ultra-efficient edge and mainframe AI computing.
   
• The aid of NVIDIA and AMD, Samsung began producing new generation AI GPUs on the 3nm and 4nm processes, which allow for more efficient power-scaled chip manufacturing. Also, Samsung is working on a full-stack solution by designing AI accelerators and high bandwidth memory for efficient AI training and has developed its own architectures for optimizing on-chip data traffic. Samsung now fills the dual role of a fabs partner and a tech contributor, aiding AI hardware innovation.
   

AI Hardware Industry News

• In July 2025, the HNSE Asia AI Hardware Battle 2025 has now extended to Japan, partnering with major retailers to showcase AI hardware innovations from one of Asia's largest technology markets. The program fosters international exposure for entrepreneurial ventures and drives the growth of high-end consumer electronics hardware.
   
• In June 2025, released alongside the iPhone 16 series, Apple’s A18 and A18 Pro chips feature advanced Neural Engines with 35 TOPS, increasing machine learning performance up to 2× compared to A16 Bionic. On-device AI tasks such as text summarization, Siri improvements, and image analysis can now be performed in real-time without compromising user privacy. By embedding advanced NPUs directly into consumer devices, Apple is responding to the AI hardware market’s need for speed and intelligence in user interaction, thereby making the incorporation of specialized AI hardware a critical driver of growth in the electronics sector.
   
• In June 2025, Nvidia's revealed Blackwell GPUs, expected to be utilized in the RTX 50 series and showcased in GTC 2025, boast a claim of up to 50 times greater efficiency over CPUs. The improvements in FLOPS, memory bandwidth, and power consumption all contribute to energy efficiency. This move reinforces the trend toward optimizing the design of GPUs for large-scale AI computations in a sustainable manner.
   
• In March 2025, with the purpose of achieving synergy with Arm and Graph core, SoftBank acquired Ampere, one of the foremost manufacturers of Arm-based AI data centre processors. This acquisition further strengthens SoftBank's AI computing conglomerate.
   

The AI hardware market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($Bn) and volume (Units) from 2021 to 2034, for the following segments.

Market, By Processor

• Graphics processing unit (GPU)
  • Training
  • Inference
  • Edge
  • Data center
• Central processing unit (CPU)
  • AI-optimized
  • Server CPU with AI acceleration
  • Edge computing
• Tensor processing unit (TPU)
  • Cloud
  • Edge
  • Custom designs
• Application-specific integrated circuit (ASIC)
  • AI training
  • AI inference
  • Custom AI

• Field-programmable gate arrays (FPGA)
  • AI-optimized
  • Edge AI
  • Reconfigurable computing platforms
• Neural processing units (NPU)
  • Smartphone
  • Edge AI
  • IoT

Market, By Memory & Storage

• High bandwidth memory (HBM)
• AI-optimized DRAM
• Non-volatile memory
• Emerging memory technologies

Market, By Application

• Data center and cloud computing 
• Automotive and transportation
• Healthcare and life sciences
• Consumer electronics
• Industrial and manufacturing
• Financial services
• Telecommunications

Market, By Deployment

• Cloud-based
• On-premises

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Philippines
  • Vietnam
  • ANZ
  • Singapore
• Latin America
  • Brazil
  • Mexico
  • Argentina
• MEA
  • UAE
  • Saudi Arabia
  • South Africa