Exponential Growth in AI Infrastructure Market

  • AI Infrastructure Market for Deep Learning Technology is Estimated to Grow at Higher CAGR During Forecast Period.
  • AI Infrastructure Market for Cloud Service Providers in APAC is Estimated to Grow at Highest CAGR During Forecast Period.
  • China Is Expected to Grow at Highest CAGR in AI Infrastructure Market During Forecast Period.

Artificial intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that usually require human intelligence. AI involves the study and synthesis of intelligent agents—in this case, a computer system. AI applications process large volumes of data, and they require powerful processing capabilities outside. AI infrastructure enables superior data throughput and storage capacity, as well as large data workloads.

AI infrastructure comprises hardware components such as processors, memory, storage devices, and networking, as well as server software. All these components deliver higher performance and improved efficiency than that of the conventional components. AI-optimized solutions can learn the patterns, relationships, transformations on their own when the data is shown to machine learning algorithms. The global AI infrastructure market is projected to grow from $14.6 billion in 2019 to $50.6 billion by 2025, at a CAGR of 23.1%.

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Key Players:

Key players operating in the AI infrastructure market are Intel Corporation (US), NVIDIA Corporation (US), IBM (US), Samsung Electronics (South Korea), Google (US), Microsoft (US), Micron Technology (US), Amazon Web Services (US), CISCO (US), Oracle (US), ARM (UK), Xilinx (US), Advanced Micro Devices (AMD) (US), Dell (US), HPE (US), Habana Labs (Israel), and Synopsys Inc. (US). Increasing adoption of cloud machine learning platform and escalating demand for AI hardware in high-performance computing data centers are driving the AI infrastructure market.

The AI infrastructure market for the hardware segment is estimated to grow at the highest CAGR during the forecast period

The AI infrastructure market based on hardware has been further segmented into a processor, memory, storage, and networking (switches, routers, and other equipment used to link servers in the cloud and to connect edge devices). NVIDIA (US), Intel (US), Micron (US), Xilinx (US), Google (US), Samsung (South Korea), Habana Technologies (Israel), and Graphcore (UK) are a few of the companies that develop hardware needed for AI.

The AI infrastructure market for deep learning is expected to grow at the highest CAGR during the forecast period

Deep learning is a class of ML based on multiple algorithms for creating relationships among data. Deep learning uses artificial neural networks to learn a representation of multiple levels of data, such as texts, images, and sounds. Its algorithms help in identifying patterns from a set of unstructured data. Presently, deep learning technology is used in voice recognition, fraud detection, recommendation engines, sentiment analysis, image recognition, motion detection, etc. Algorithms help in identifying patterns from a set of unstructured data. Deep learning uses artificial neural networks to learn multiple levels of data.

The AI infrastructure market for inference function is estimated to grow at a higher CAGR during the forecast period

Inference is sensitive to latency, and the trained model needs to analyze and provide analysis in near real time. The requirement of the infrastructure for model deployment to accelerate the data at the fastest rate is expected to drive the market for inference.

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Among all regions, the market in APAC is expected to witness the highest CAGR during the forecast period

The market in APAC mainly constitutes major economies such as Singapore, South Korea, Japan, China, India, and Australia, which are expected to register high growth in the AI infrastructure market. APAC is the host to a few of the fastest-growing and leading industrialized economies such as China, Japan, and India in the world. It is witnessing dynamic changes in the adoption of new technologies and advancements in organizations across industries. Increasing adoption of deep learning and NLP technologies for finance, agriculture, marketing, and law applications is also driving the market in this region.

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Emma R

Helping CXO find how changing revenue mix of their clients and client's client is likely to impact their revenue decisions


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