Accelerator Card Market Share and Forecast to 2030

Accelerator Card Market Share has been expected to grow at a CAGR of 40.47%, with a value of USD 28,995.7 million over the forecasted 2019-2025.

The overview of the Accelerator Card Market

The worldwide Accelerator Card Market Share has been expected to grow at a CAGR of 40.47%, with a value of USD 28,995.7 million over the forecasted 2019-2025.

An accelerator card is normally used in the cloud servers for high-performance computing and data centers to accelerate several workloads. The accelerator cards can be plugged in through the PCIe slot, and the programmable enables the user to instruct the card to perform several tasks. The accelerator cards are more efficient compared to the general-purpose microprocessors. GPUs and CPUs are some of the extensively used accelerator cards in higher performance computing and data centers. From scientific discoveries to artificial intelligence, modern data centers use these accelerator cards to solve some of the world’s most critical challenges. Such advanced data centers are transforming to increase the networking bandwidth and optimize workloads like artificial intelligence. The data centers administrators also expect a lower total cost of the ownership, lower power and new services.

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Accelerators enable the customers to meet their demands. They are specially designed to solve customer problems, with high-performance and hardware-based acceleration with excellent cost and power efficiency. As businesses are increasingly applying artificial intelligence technologies to differentiate and advance their process and providing, the enterprises are implementing machine learning applications like image and voice recognition, CPUs and GPUs. They have relied on more for faster training real-time interface. Such advanced processors can work for the increased network bandwidth created by the AI and MI workloads.

From the machine learning interface, video transcoding, and data analytics to computational storage, electronic trading, and financial risk modeling, enterprises are actively looking for programmability, flexibility, high throughput, and low latency performance advantages over any other server deployment centers.

Competitive Analysis

The Key Players of Global Accelerator Card Market are NVIDIA Corporation (Germany), Intel Corporation (US), Advanced Micro Devices Inc. (US), Xilinx Inc. (US), Achronix Semiconductor Corporation (US), Cisco Systems Inc (US), FUJITSU LTD (Japan), NVIDIA Corporation. (US), Oracle Corporation (US), HP Development Company, L.P (US), Huawei Technologies Co. Ltd (China), IBM Corporation (US), and Kalray Corporation (US), among others.

Segmental Analysis

Global Accelerator Card Market has been segmented based on Processor Type, Accelerator Type, Application, and Region.

Based on Accelerator Type, the market has been classified into a high-performance computing accelerator, cloud accelerator. The high-performance computing accelerator segment accounted for the larger market share in 2018, with the highest market value. Accelerator cards are used in high-performance computing (HPC) to accelerate the work processes and reduce the workload on the system. HPC uses a cluster of computers to solve advance computational problems in data warehouses, line-of-business, and transaction processing applications. Cloud acceleration helps data centers to support local functions such as accelerating networking/infrastructure functions, including the encryption of network flows.

Based on Application, the market has been classified into video and image processing, machine learning, financial computing, data analytics, mobile phones, others. Although these cards are integrated with memory and digital-to-analog converters, they do not support data processing capabilities. Over the years, there has been a significant increase in video content on the Internet, which has driven the need for techniques that can sort, classify, identify, and process images. Financial computing requires efficient algorithms and implementation to accelerate calculations. However, these solutions consume more computational power and energy. The need for data processing has increased enormously since more and more data has been generated from mobile sources such as vehicles and smartphones. Smartphones are integrated with several components, such as processors, memory, and graphic accelerator.