AI Infrastructure Market worth $394.46 billion by 2030 - Exclusive Report by MarketsandMarkets™

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AI Infrastructure Market Report Scope:

By offerings, network segment is projected to grow at a high CAGR of AI infrastructure market during the forecast period.

Network is a crucial element in the AI Infrastructure. It is used for the effective flow of data through the processing unit, storage devices, and interconnecting systems. In AI-driven environments where voluminous data has to be processed, shared, and analyzed in real time, a high-performance, scalable, and reliable network is needed. Without an efficient network, AI systems would struggle to meet the performance requirements of complex applications such as deep learning, real-time decision-making, and autonomous systems. The network segment includes NIC/ network adapters and interconnects. The growing need for low-latency data transfer in AI-driven environments drives the growth of the NIC segment. NICs and network adapters enable AI systems to process large datasets in real-time, thus providing much faster training and inference of the models. For example, Intel Corporation (US) unveiled Gaudi 3 accelerator for enterprise AI in April 2024, that supports ethernet networking. It allows scalability for enterprises supporting training, inference, and fine-tuning. The company also introduced AI-optimized ethernet solutions that include AI NIC and AI connectivity chips through the Ultra Ethernet Consortium. Such developments by leading companies for NIC and network adapters will drive the demand for AI infrastructure.

By function, Inference segment will account for the highest CAGR during the forecast period.

The AI infrastructure market for inference functions is projected to grow at a high CAGR during the forecast period, due to the widespread deployment of trained AI models across various industries for real-time decision-making and predictions. Inference infrastructure is now in higher demand, with most organizations transitioning from the development phase to the actual implementation of AI solutions. This growth is driven by the adoption of AI-powered applications in autonomous vehicles, facial recognition, natural language processing, and recommendation systems, where rapid and continuous inference processing is important for the operational effectiveness of the application. Organizations are investing heavily in support of inference infrastructure in deploying AI models at scale to optimize operational costs and performance. For example, in August 2024 Cerebras (US) released the fastest inference solution, Cerebras Inference. It is 20 times faster than GPU-based solutions that NVIDIA Corporation (US) offers for hyperscale clouds. The quicker inference solutions allow the developers to build more developed AI applications requiring complex and real-time performance of tasks. The shift toward more efficient inference hardware, including specialized processors and accelerators, has made AI implementation more cost-effective and accessible to a broader range of businesses, driving AI infrastructure demand in the market.

By deployment- hybrid segment in AI infrastructure market will account for the high CAGR in 2024-2030.

The hybrid segment will grow at a high rate, due to the need for flexible deployment strategies of AI that caters to various aspects of businesses, especially sectors dealing with sensitive information and require high-performance AI. hybrid infrastructure allows enterprises to maintain data control and compliance for critical workloads on-premises while offloading tasks that are less sensitive or computationally intensive to the cloud. For example, in February 2024, IBM (US) introduced the IBM Power Virtual Server that offers a scalable, secure platform especially designed to run AI and advanced workloads. With the possibility to extend seamless on-premises environments to the cloud, IBM's solution addresses the increasing need for hybrid AI infrastructure combining the reliability of on-premises systems with the agility of cloud resources. In December 2023, Lenovo (China) launched the ThinkAgile hybrid cloud platform and the ThinkSystem servers, which are powered by the Intel Xeon Scalable Processors. Lenovo's solutions give better compute power and faster memory to enhance the potential of AI for businesses, both in the cloud and on-premises. With such innovations, the hybrid AI infrastructure market will witness high growth as enterprises find solutions that best suit flexibility, security, and cost-effectiveness in an increasingly data-driven world.

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North America region will hold highest share in the AI Infrastructure industry.

North America is projected to account for the largest market share during the forecast period. The growth in this region is majorly driven by the strong presence of leading technology companies and cloud providers, such as NVIDIA Corporation (US), Intel Corporation (US), Oracle Corporation (US), Micron Technology, Inc (US), Google (US), and IBM (US) which are heavily investing in AI infrastructure. Such companies are constructing state-of-the-art data centers with AI processors, GPUs, and other necessary hardware to meet the increasing demand for AI applications across industries. The governments in this region are also emphasizing projects to establish AI infrastructure. For instance, in September 2023, the US Department of State announced initiatives for the advancement of AI partnering with eight companies, including Google (US), Amazon (US), Anthropic PBC (US), Microsoft (US), Meta (US), NVIDIA Corporation (US), IBM (US) and OpenAI (US). They plan to invest over USD 100 million for enhancing the infrastructure needed to deploy AI, particularly in cloud computing, data centers, and AI hardware. Such innovations will boost the AI infrastructure in North America by fostering innovation and collaboration between the public and private sectors.

Key Players

Key companies operating in the AI infrastructure companies are NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), SK HYNIX INC. (South Korea), SAMSUNG (South Korea), Micron Technology, Inc. (US), Intel Corporation (US), Google (US), Amazon Web Services, Inc. (US), Tesla (US), Microsoft (US), Meta (US), Graphcore (UK), Groq, Inc. (US), Shanghai BiRen Technology Co., Ltd. (China), Cerebras (US), among others.

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