AI Infrastructure Professional Market is projected to reach at USD 50.6 billion, at a CAGR of +23% by the end of 2028.
AI infrastructure encompasses almost every stage of the machine learning workflow. It enables data scientist, data engineers, software engineers and DevOps teams to access and manage the computing resources to test, train and deploy AI algorithms.
Seamless networking is an essential component of AI infrastructure. As scalability is of high priority, organization would require a high-bandwidth, low-latency network. Deep learning algorithms depend on communications, and as AI experiment progresses and expand, network system needs to keep pace.
Also critical for an artificial intelligence infrastructure is having sufficient compute resources, including CPUs and GPUs. A CPU-based environment can handle basic AI workloads, but deep learning involves multiple large data sets and deploying scalable neural network algorithms.
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Top Key Players:
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).
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Based on Offering
- Server Software
Based on Technology
- Machine learning
- Deep learning
Based on Function
Based on Deployment type
Based on End-user
- Government organizations
- Cloud service providers
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