Data Center Accelerator Industry

The report "Data Center Accelerator Market by Processor (GPU, CPU, ASIC, FPGA), Type (Cloud Data Center, HPC Data Center), Application (Deep Learning Training, Enterprise Inference), End-user (IT & Telecom, Healthcare, Energy) and Region - Global Forecast to 2029" The global data center accelerator market was valued at USD 109.9 billion in 2024 and is projected to reach USD 372.9 billion by 2029; it is expected to register a CAGR of 27.7% during the forecast period. Growing demand for cloud-based services, rising focus on parallel computing in AI data centers, and surging adoption of deep learning technology in big data analytics are attributed to the demand of data center accelerators.

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By End-user: IT & Telecom segment to account for the largest market share in the forecasted year.

The IT & Telecom segment accounted for the largest share of the data center accelerator market in 2029. The segment's growth is attributed to the the exponential growth of data and the need for faster, more efficient data processing capabilities. As businesses increasingly rely on big data analytics, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) applications, traditional data center architectures struggle to keep up with the intensive computational and storage requirements. Accelerators such as GPUs, FPGAs, and ASICs offer significant performance improvements by offloading and optimizing specific tasks, enabling higher throughput and lower latency.

By Type: Cloud data centers accounted for a larger market share in the forecasted year.

Cloud data centers accounted for a larger share in the forecasted period. The increasing adoption of AI is causing changes in cloud server configuration. The cloud computing market has seen significant growth in recent years due to the increased volume of data transferred to the cloud from consumers. This has led to a rising demand for co-processors (accelerators) as they optimize server data processing by reducing latency. In AI-powered servers used for deep learning training, the typical CPU-to-GPU attachment rate is 1–4 GPUs; in some cases, it is around 1–8 GPUs. Deep learning is expected to dominate cloud workloads in the coming years, leading to increased demand for accelerators in cloud servers. It is projected that in the near future, over one-third of servers shipped will be dedicated to either deep learning training or deep learning inference. Additionally, accelerators are expected to be widely employed in public and enterprise cloud servers to enhance inference applications.

Leading cloud service providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, operate extensive networks of these data centers globally. Similarly, Xilinx Alveo U250 Data Center accelerator cards are designed to meet the constantly changing needs of modern data centers, providing up to 90X higher performance than CPUs for key workloads, including machine learning inference, video transcoding, and database search and analytics. Built on the Xilinx 16 nm Ultra Scale architecture, Alveo accelerator cards are adaptable to changing acceleration requirements and algorithm standards, capable of accelerating any workload without changing hardware and reducing the overall cost of ownership. The key benefit of Xilinx Alveo is that it can be accessed for both cloud and on-premises mobility deployment.

North America is expected to hold the largest share of the data center accelerator market during the forecast period.

North America captured the largest share of the data center accelerator market in the forecast period. This growth is attributed to the presence of a large number of data centers in the region implying the demand for data center accelerator processors such as GPU’s and ASIC’s. Major Cloud service providers have their headquarters in this region such as AWS (US), Microsoft (US), and Alphabet Inc. (US).

Key Players

The key companies that offer virtual reality devices include NVIDIA Corporation (US), Advanced Micro Devices, Inc (US), Intel Corporation (US), Alphabet Inc. (US), Qualcomm Technology, Inc. (US).

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