Deep Learning Market Size to Surpass USD 1255.81 BN by 2034
The deep learning market size was exhibited at USD 69.32 billion in 2024 and is projected to hit around USD 1255.81 billion by 2034, growing at a CAGR of 33.6% during the forecast period 2024 to 2034.
The technology is gaining prominence because of advancements in data center capabilities, high computing power, and its ability to perform tasks without relying on human input. Moreover, the rapid adoption of cloud-based technology across several industries is fueling the growth of the deep learning industry.
The U.S. deep learning market size is evaluated at USD 19.24 billion in 2024 and is projected to be worth around USD 350.81 billion by 2034, growing at a CAGR of 30.2% from 2024 to 2034.
North America dominated the market with a revenue share of over 37.0% in 2024, which is attributed to increased investments in artificial intelligence and neural networks. The high adoption of image and pattern recognition in the region is expected to open new growth opportunities over the forecast period. Moreover, the region is one of the early adopters of advanced technologies, rendering organizations to adopt deep learning capabilities at a faster pace.
Furthermore, increased government support is expected to provide a positive impact on the growth of the industry in the region. The establishment of subcommittees on artificial intelligence and machine learning within the federal government is providing traction for growth.
Europe has contributed significantly to the market growth as several new measures have been taken to support the artificial intelligence sector in the region to boost growth and deliver a digital economy. This, in turn, has offered considerable growth opportunities in the deep learning space. The U.K. is underpinning the technology to grow further in the areas of autonomous vehicles, smart devices, and cyber security.
| Report Coverage | Details |
| Market Size in 2025 | USD 92.61 Billion |
| Market Size by 2034 | USD 1255.81 Billion |
| Growth Rate From 2024 to 2034 | CAGR of 33.6% |
| Base Year | 2024 |
| Forecast Period | 2024-2034 |
| Segments Covered | Solution, Hardware, Application, End-use, Region |
| Market Analysis (Terms Used) | Value (US$ Million/Billion) or (Volume/Units) |
| Regional scope | North America; Europe; Asia Pacific; Latin America; MEA |
| Key Companies Profiled | Advanced Micro Devices, Inc.; ARM Ltd.; Clarifai Inc.; Entilic; Google, Inc.; HyperVerge; IBM Corporation; Intel Corporation; Microsoft Corporation; NVIDIA Corporation |
The software segment led the market and accounted for a revenue share of more than 48% in 2024. The number of software tools for developers has grown significantly over the last few years. As a result, companies are developing deep learning frameworks through a high level of programming, powerful tools, and libraries that will help design, train, and validate deep neural networks. Moreover, the ONNX architecture, machine comprehension, and edge intelligence further enhance the deep learning experience across organizations.
Various startups and established companies focus on new hardware innovations to support efficient deep learning processing. Wave Computing, Inc.; Cerebras Systems Inc.; and Mythic are some of the startups working on developing deep learning chipsets and hardware. Investors and big corporate companies are also showing keen interest in these startups, accelerating the growth of deep-learning technology.
For instance, in July 2018, Xilinx, Inc. acquired DeePhi Technology Co., Ltd., a Beijing-based startup company working to develop neural networks and provide end-to-end applications on deep-learning processor unit (DPU) platforms.
The Graphics Processing Unit (GPU) held the largest market share of around 56.3% in 2024. GPUs are a widely used hardware category for improving training and classification processes in Computer Neural Networks (CNNs) as it holds high memory bandwidth and throughput. Moreover, GPU provides better computational ability allowing the system to do multiple parallel processes. Multi-GPU enhances deep learning performance by combining several GPUs in one computer.
Moreover, it offers a fast and accurate computational ability to perform a broad set of tasks concurrently in real-time. Multi-GPU helps in object detection for the autonomous car. The system needs to perform a comprehensive set of tasks in quick successions, such as detecting obstacles, determining boundary lines, and intersection detection. Several innovations are advancing deep learning. For instance, In May 2020, NEUCHIPS corporation announced the world's first deep learning recommendation model called RecAccelTM. This can perform 500,000 inferences per second.
FPGA has emerged as the best possible choice for deep learning technology. FPGA configurations were once only used for training, but they are now widely employed for various applications. FPGA is flexible, fast, power-efficient, and offers a good application for data processing in data centers. Moreover, FPGAs have gained prominence among engineers and researchers as they help to swiftly prototype several designs in significantly faster periods than a traditional IC.
Image recognition held the largest market share of around 41.0% in 2024. Deep learning can be used in stock photography and video websites to make visual content discoverable for the user. The technology can be used in visual search, allowing users to search for similar images or products using a reference image. Moreover, the technology can be used in medical image analysis, facial recognition for security and surveillance, and image detection on social media analytics.
The increasing visual content on social media and the need for content modernization will drive image recognition applications' deep learning market. For instance, in 2018, Instagram announced a new feature based on deep learning algorithms for describing photos to users with visual impairments.
The feature automatically identifies the photo using image recognition technology and then reads its automated description of the photo. Also, in March 2021, Facebook developed a deep learning solution called SEER (Self-supERvised). This solution can autonomously work its way through the dataset and can learn from any random group of unlabeled images on the internet.
The data mining application is expected to grow at the highest CAGR, over 37%, during the forecast period. Deep learning can address the challenges during data mining and extraction processes, such as fast-moving streaming data, the trustworthiness of data analysis, imbalanced input data, and highly distributed input sources. A deep learning algorithm helps in semantic indexing and tagging videos, text, and images and performs the discriminative task. Deep learning possesses the ability to execute the featured engineering to perform a complex task and provide better data representation.
In November 2019, the Securities and Exchange Board of India (SEBI) announced the plan to invest USD 70 million in information technology over the next five years, focused on implementing advanced analytical tools such as machine learning, deep learning, and big data analytics for stock market prediction, data mining, and processing of unstructured data.
The automotive end-use industry contributed around 12.83% of revenue share in 2024. The autonomous vehicle is a revolutionary technology that requires a massive amount of computation power. A Deep Neural Network (DNN) can rapidly help the autonomous vehicle perform various tasks without the need for human input.
The autonomous vehicle is expected to gain momentum in the coming years, and thus various startups and large companies are working on its development. Google Inc., Uber Technologies, Inc., and Tesla, Inc. are some prominent companies showing their capabilities in developing autonomous vehicles. As a result, in December 2019, Nvidia launched the NVIDIA DRIVE platform for autonomous vehicles.
Various investments are being made to enhance the use of deep learning to improve the features of autonomous vehicles. For instance, in January 2024, Wayve, a London-based startup, raised USD 200 million. This will help the organizations create deep learning techniques to train and develop artificial intelligence capable of complex driving situations.
The healthcare segment is expected to witness the strongest growth over the forecast period. Digital transformation in the healthcare industry is expected to continue for the next few years, providing an opportunity for innovative technologies such as AI, deep learning, and data analytics to intervene in the industry. Deep learning can be used in predictive analytics, such as early detection of diseases, identifying clinical risk and its drivers, and predicting future hospitalization.
Moreover, several government initiatives to integrate AI and deep learning in healthcare are expected to drive the market over the forecast period. Currently, NITI Aayog in India is working on implementing DNN models for the early diagnosis and detection of diabetic and cardiac risk. FDA is also working on a regulatory framework to implement AI and machine learning in the healthcare industry.
This report forecasts revenue growth at country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2034. For this study, Nova one advisor, Inc. has segmented the deep learning market
By Solution
By Hardware
By Application
By End-use
By Regional
Cross-segment Market Size and Analysis for Mentioned Segments
Additional Company Profiles
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Additional Countries (Apart
From Mentioned Countries)
Country/Region-specific Report
Go To Market Strategy
Region Specific Market DynamicsRegion Level Market Share Import Export AnalysisProduction AnalysisOthers