AI Infrastructure Market Size 2026 Industry Share, Growth Analysis, Regional Demand, Revenue

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MarketsandMarkets Presents

AI Infrastructure Market Size 2026 Industry
Share, Growth Analysis, Regional Demand,
Revenue AI Infrastructure Market with COVID-19
Impact Analysis by Offering (Hardware, Software),
Technology (Machine Learning, Deep Learning),
Function (Training, Inference), Deployment Type
(On-Premises, Cloud), End User, and Region -
Global Forecast to 2026 https//www.marketsandmar
kets.com/Market-Reports/ai-infrastructure-market-3
8254348.html
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The AI Infrastructure Market is expected to grow
from USD 23.7 billion in 2021 to USD 79.3
billion by 2026, at a CAGR of 27.3. The market
growth can be attributed to several factors, such
as increased data traffic and need for high
computing power, increasing adoption of cloud
machine learning platform, increasingly large and
complex dataset, rising focus on parallel
computing in AI data centers, and growing number
of cross-industry partnerships and
collaborations. Browse 169 market data Tables
and 60 Figres spread through 238 Pages and
in-depth TOC on "AI Infrastructure Market with
COVID-19 Impact Analysis by Offering (Hardware,
Software), Technology (Machine Learning, Deep
Learning), Function (Training, Inference),
Deployment Type (On-Premises, Cloud), End User,
and Region - Global Forecast to 2026"
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Hardware to account for the largest share of AI
infrastructure market in 2021 The hardware
segment is sub-segmented into a processor,
memory, storage, and networking (switches,
routers, and other equipment used to link servers
in the cloud and connect edge devices). NVIDIA
(US), Intel (US), Micron (US), Xilinx (US),
Google (US), Samsung (South Korea), and Graphcore
(UK) are a few of the companies that develop
hardware needed for AI. The increasing need for
hardware platforms with high computing power to
run various AI software is a key factor fueling
the growth of the AI infrastructure market. Ask
PDF Brochure https//www.marketsandmarkets.com/pd
fdownloadNew.asp?id38254348 Machine learning
technology held the largest share of AI
infrastructure market in 2020 Machine learning
enables systems to learn without being explicitly
programmed. This technology can reliably and
quickly scan, parse, and react to anomalies.
Machine learning enables systems to automatically
improve their performance with experiences.
Recently, AI and machine learning have been
widely adopted to tackle the COVID-19 crisis.
Several organizations are using machine learning
to scale up customer communications, understand
the spread of COVID-19, and help accelerate
research and treatment processes.
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Asia Pacific to grow at the highest CAGR in 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. Request Free Sample Pages https//www.ma
rketsandmarkets.com/requestsampleNew.asp?id382543
48 Some of the key companies operating in the
market are Intel Corporation (US), NVIDIA
Corporation (US), IBM (US), Xilinx (US), Advanced
Micro Devices (AMD) (US), Samsung Electronics
(South Korea), Micron Technology (US), Google
(US), Microsoft (US), Amazon Web Services (US)
and so on.
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Opportunity Surging demand for FPGA-based
accelerators Field Programmable Gate Array (FPGA)
is an integrated circuit that a customer or
designer can configure after it is being
manufactured (field programmable). FPGAs are
programmed using hardware description languages
such as VHSIC hardware description language
(VHDL) or Verilog. FPGAs offer advantages such as
rapid prototyping, short time-to-market, the
ability to be reprogramed in the field for
debugging, and a long product life cycle. They
contain individual programmable logic blocks
known as configurable logic blocks (CLBs). These
logic blocks are interconnected in such a manner
that a user can configure the computing system
multiple times. FPGAs contain large resources of
logic gates and RAM to perform complex digital
computation. FPGAs are used as co-processors to
offload work done in microcontrollers, digital
signal processors, or any other host processor.
FPGAs provide flexible interfacing and are
optimized to complement host processors. Challeng
e Concerns regarding data privacy in AI
platforms AI has several applications in the
healthcare industry. However, the adoption of AI
in the industry is restricted to an extent owing
to data privacy concerns. Patients health data
is protected under federal laws in many
countries, and any breach or failure to maintain
its integrity can result in legal and financial
penalties. As AI used for patient care requires
access to multiple health datasets, it is
essential for AI-based tools to adhere to all
data security protocols mandated by governments
and regulatory authorities. This is a challenging
task as most AI platforms are consolidated and
require extensive computing power owing to which
patient data, or parts of it, can be required to
reside in a vendors data center. This is a major
challenge in the market. The figure provided
below shows the percentage of healthcare breaches
reported by the US Department of Health and Human
Services, which has affected more than 500
individuals.
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