The global AI infrastructure market size had a revenue holding of USD 38.34 billion in 2021. It is expected to reach USD 172.9 billion by 2030, growing at a CAGR of 20.72% during the forecast period (2022–2030).
Data center resource management is increasingly dependent on artificial intelligence (AI). As more systems use AI technology, IT employees can better design, implement, maintain, and protect environments. AI has produced the concept of AI-defined infrastructure because it is so valuable. This intelligent solution streamlines IT infrastructure management by combining advanced analytics, self-learning, and automation. An AI-defined infrastructure system collects data from all of an IT infrastructure's systems and then prepares the data for analysis. It combines predictive analytics with AI technologies like machine learning and deep learning to perform such analysis. The artificial intelligence system then uses the data to forecast outcomes and automate administrative chores, working in tandem with software-defined infrastructure technologies.
AI infrastructure necessitates many resources that can give an adequate performance with computational resources such as CPUs and GPUs, a large storage capacity, and advanced networking infrastructure. AI infrastructure comprises nearly all phases of the process for machine learning. It allows data scientists, software engineers, and DevOps teams to acquire and manage the computing resources necessary for testing, training and deploying AI algorithms. The expansion of this market is fueled by factors such as increased data traffic and the demand for high processing power, the increasing acceptance of cloud-based machine learning platforms, increasingly extensive and complex datasets, the growing number of cross-industry partnerships and collaborations, the expanding adoption of AI due to the pandemic and the increasing importance of parallel computing in AI data centers.
|Market Size||USD 172.9 billion by 2030|
|Fastest Growing Market||Asia-Pacific|
|Largest Market||North America|
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
The foundational data center infrastructure is under great stress due to the exponential expansion of smart connected devices and a sharp increase in data consumption. It is now impossible for just humans to handle the increasing complexity of data centers due to their increased complexity. Data center hardware with artificial intelligence capabilities has the potential to significantly increase the effectiveness of data operation. It is advisable to carry out the computationally difficult task of training an ML model on millions of datasets at data centers. GPUs (graphic processing units) have completed this task, and new hardware is expanding the options.
In data centers, CPUs are utilized for serial computing to keep track of several memory regions where data and instructions are maintained. A processor analyzes the instructions and data at the memory addresses to perform computations in serial. The steps of a calculation are logically ordered and sequential in serial analysis. In other words, a processor at a data center divides a single task into several different instruction sets that are carried out serially. This frequently causes latency issues in data centers, especially when performing AI-based calculations with extensive data and instruction sets. The parallel computing framework allows concurrently utilizing numerous compute resources to carry out instructions. Using this technique, instructions are broken up into distinct chunks that can be processed simultaneously by several co-processors. Due to this, HPC/supercomputers benefit from parallel processing.
Commercial servers increasingly embrace parallel computing as AI, data mining, and virtual reality advance. Due to their parallel architecture and tens of thousands of cores, GPUs are well suited for parallel computing since they can process many instructions simultaneously. The parallel computing paradigm is perfect for implementing deep learning training and interface because, on the whole, parallel computing is more effective for artificial neural networks. The market for AI infrastructure is anticipated to rise during the anticipated period due to the rising demand for parallel computing.
Companies need expertise and a competent team to create, manage, and integrate AI systems because they are complicated systems. Additionally, it is a complex undertaking that necessitates well-funded internal research & development and patent filing to integrate AI technology into already-existing systems. Even small mistakes can result in system failure or solution malfunction, significantly impacting the outcome and desired outcome. Experts' data scientists and developers are required to adapt current ML-enabled AI processors. Businesses from all sectors adopt emerging technologies to increase operational effectiveness and efficiency, cut waste, protect the environment, quickly and easily reach new audiences, and support product and process innovation.
According to Moore's law, integrated circuits would double the number of transistors per square inch roughly every 18 months until 2020. In 2015, Intel Corporation claimed that by creating 7 nm and 5 nm fabrication technologies, it might continue Moore's law for a few more years. It would be difficult to further shrink the size of processors in the future because doing so would also shorten the distance between electrons and holes, leading to issues like current leakage and overheating in integrated circuits (ICs). These issues would result in decreased durability, slower performance, and increased power consumption of ICs. Therefore, the creation of accelerators or co-processor chips, which are essential components of the infrastructure for AI, was motivated by the need to find a different technique to improve the processing capability of chips.
By region, the global AI infrastructure market is analyzed across North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa.
North America will command the market, expanding at a CAGR of 20% over the forecast period. The growth in the region is majorly attributable to the presence of nations such as the United States and Canada. The development of AI in the North American region has been aided by the United States' robust innovation ecosystem, which is supported by strategic federal investments in cutting-edge technology, as well as the existence of visionary scientists and entrepreneurs who come together from around the world and top research institutions. Additionally, the region is seeing a significant increase in connected, 5G, and IoT devices. As a result, network slicing, virtualization, novel use-cases, and service needs are needed by Communications Service Providers (CSPs) to effectively handle an ever-increasing complexity. Due to the unsustainable nature of conventional network and service management strategies, this is anticipated to boost demand for AI solutions.
Asia-Pacific is envisioned to reach USD 57 billion by 2030, growing at a CAGR of 22.2%. Due to the existence of populous nations such as China and India, Asia-Pacific has experienced rapid economic expansion. One of the economies with the most remarkable growth rate is India, which has a keen interest in the global advancement of AI. The Indian government is making every effort to guide the nation and establish it as a leader in AI because it understands the potential. The government is attempting to overcome this advantageous ecology to advance AI quickly. Similarly, to support information services for the rising market, the Chinese government is accelerating the construction of new infrastructure projects, including 5G networks and data centers. The Next Generation Artificial Intelligence Development Plan, which pledges governmental support, centralized coordination, and investments of more than USD 150 billion by 2030, was also established, as announced by the government.
The global AI infrastructure market is classified into offering, deployment, end-user, and region.
By offering, the global AI infrastructure market is segmented into Hardware and Software.
The Hardware section is expected to expand at a CAGR of 19.85% and hold the largest market share over the forecast period. The category is further sub-segmented into Processor, Storage, and Memory. The hardware segment is mainly driven by the growing demand for the processor. Field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and graphics processing units (GPUs) are examples of AI-specific chips. Central processing units (CPUs), a type of general-purpose microprocessor, can be utilized for some rudimentary AI tasks, but as AI develops, CPUs become less and less effective. While typically, when it comes to processing AI, GPUs outperform CPUs. For the industry to handle AI applications, inference, and modeling effectively, specialized processors are needed. Chip designers are actively developing processing units that are tailored for running these algorithms as a result.
The Software section will hold the second-largest market share. Examples are machine learning, virtual assistants, speech and voice recognition, business intelligence platforms, and other AI software features. Software that uses artificial intelligence (AI) develops intelligence levels by learning numerous data patterns and insights that are continuously updated through algorithm training, producing more intelligent software. The AI software sector includes applications using artificial intelligence, such as chatbots, computer vision technologies, or various data analytics tools.
By deployment, the global AI infrastructure market is segmented into On-premiss, Cloud, and Hybrid.
The Hybrid section is expected to hold the largest market share, growing at a CAGR of 21.71% by 2030. The demand for on-premise solutions that support scalability, both vertically and horizontally, is increasing as AI solution providers increasingly move from SMEs to major corporations. Due to this, businesses are increasing the demand for hybrid integration solutions, combining on-premise applications and cloud-based services. The key benefit of employing a hybrid architecture for AI solutions is that businesses may scale them up or down depending on the activities or applications they are using them for.
The Cloud section will hold the second-largest market share. Integrating AI with cloud computing, organizations have begun implementing the AI cloud, which was initially only a concept. The adoption of AI is influenced by several key variables, some of which include AI tools and software that bring new, more significant value to cloud computing, which is not just a cost-effective choice for data storage and computation but also has an impact on it. The problems that AI in the cloud solves are among its most appealing advantages.
By end-user, the global AI infrastructure market is segmented into Enterprises, Government, and Cloud Service Providers.
The Cloud Service Providers section is expected to hold the largest market share, growing at a CAGR of 21% by 2030. Businesses operating all over the world who wish to employ AI technology have encountered significant obstacles because it is too expensive for them to build AI infrastructure internally. As a result, there is a high demand for outsourcing AI technology. A solution for AI has been made available by the major cloud service providers. They have built AI infrastructure to offer these cutting-edge solutions by utilizing their considerable technological know-how and financial resources. Market suppliers have introduced new items to provide businesses with the required technology.
The Enterprises section will hold the second-largest market share. New levels of automation have been achieved across the board, from automobiles and self-service kiosks to power grids and banking networks. To automate the world, an organization must first automate itself, which has become essential. The speed at which these new environments are supplied, optimized, and decommissioned will swiftly exceed the capabilities of human operators as data loads grow larger and more complex and the infrastructure expands outside of data centers into the cloud and edge.