Accelerator Card Market Size, Share, and Industry Analysis By Processor Type (CPU, GPU, Field Programmable Gate Array (FPGA), and Application-Specific Integrated Circuit (ASIAC)), By Accelerator Type (Cloud Accelerator and High-Performance Computing Accelerator), By Application (Machine Learning, Video and Image Processing, Data Analytics, and Others (Financial Computing)), and Regional Forecast 2025-2032

The global accelerator cards market is experiencing substantial expansion due to the rising need for powerful computing solutions in sectors such as AI, gaming, telecom, and data centers. Acceleration cards, recognized for their capacity to improve processing strength and speed, are becoming crucial elements in tackling the computational hurdles of contemporary applications. The growth of AI, machine learning, and big data analysis is driving the demand for faster and more expandable hardware, making accelerator cards a vital answer.

Developments in GPU, FPGA, and NPU technologies, combined with improvements in manufacturing techniques, are reducing expenses and increasing their usage. Accelerator cards play a crucial role in advancing the next generation of computing technologies, as industries focus on energy efficiency, faster processing, and improved performance.

Impact of Generative AI on the Accelerator Card Market

Generative AI is driving the expansion of the accelerator card industry by creating a need for advanced, high-speed computing options that can manage demanding tasks such as manipulating large language models and generating images. These duties need highly developed equipment, which is pushing advancements in GPUs, TPUs, and AI-focused FPGAs. Manufacturers are being driven by the demand for scalability, efficiency, and cost-effectiveness to create AI-optimized accelerator cards for hyperscale data centers and enterprises, allowing for extensive adoption of generative AI. For instance,

• In 2024, Neuchips launched the Viper GenAI PCIe Card at COMPUTEX 2024, a plug-and-play AI accelerator designed for generative AI tasks. It enhances large language model (LLM) performance, improves RAG accuracy, and includes 48GB on-premises memory for secure data handling.

Accelerator Card Market Driver

Rise in Data Centers is a Major Driver for the Market

With the increasing demand for cloud services, big data analytics, and AI-driven applications, data centers must have advanced computing solutions to manage large workloads. Accelerator cards, such as GPUs, NPUs, and FPGAs, play a crucial role in enhancing processing speeds and energy efficiency within data centers. The growing need for quicker, more adaptable options is driving the use of accelerator cards in data centers to handle complex tasks and vast amounts of data efficiently, leading to market expansion. For instance,

• As per industry report, in December 2023, there were approximately 10,978 data center locations worldwide, with the top 20 countries leading the way, including the U.S. (5,388), Germany (522), the U.K. (517), China (449), Canada (336), France (315), and Australia (306).

Accelerator Card Market Restraint

Power and Cooling Requirements May Hinder Market Growth

Accelerator cards, especially those used in high-performance computing (HPC) and AI applications, consume significant amounts of power, often requiring hundreds of amps at low voltages. These extensive power requirements create significant heat, requiring advanced and efficient cooling systems to uphold ideal operating conditions. In data centers, it becomes a critical challenge to ensure sufficient cooling and effectively manage power consumption when deploying multiple accelerator cards in dense configurations. This results in higher operational expenses, since power and cooling systems need to be expanded to accommodate these energy-intensive requirements. Furthermore, issues with power distribution and cooling can impact the overall functioning and longevity of the hardware. For instance,

• According to an article by EE Power, AI accelerator cards require extremely high power to process AI workloads, with certain models delivering over 1000 A at sub-1V levels. These cards often face high current transients and voltage spikes that can disrupt system performance. For instance, AI chips may experience power demands of up to 650 A continuously, with peak current exceeding 1000 A.

Accelerator Card Market Opportunity

Cryptocurrency Mining Creates an Opportunity for the Market

The cryptocurrency mining industry presents a significant market opportunity for accelerator cards, particularly GPUs and ASICs, due to the high computational power required for solving complex cryptographic puzzles in proof-of-work algorithms. Cryptocurrencies such as Bitcoin and Ethereum are evolving, leading to a higher mining difficulty that requires more advanced hardware such as GPUs and custom ASICs. In bull markets, the increase in mining activity boosts the need for high-performance accelerator cards, particularly in large-scale mining operations and mining pools.

Furthermore, the increasing demand for energy-efficient hardware due to growing regulatory oversight and expanding mining activities worldwide is also driving the market, creating ongoing possibilities for advancement and expansion in the accelerator card industry. For instance,

• In 2022, Intel entered the Bitcoin mining industry with a new energy-efficient blockchain accelerator chip, designed to significantly enhance performance while reducing energy consumption. The chip promises a 1000x improvement in performance per watt compared to traditional GPUs used for SHA-256 mining.

Segmentation

Key Insights

The report covers the following key insights:

• Micro Macro Economic Indicators
• Drivers, Restraints, Trends, and Opportunities
• Business Strategies Adopted by Key Players
• Impact of Generative AI on the Accelerator Card Market
• Consolidated SWOT Analysis of Key Players

Analysis by Processor Type

Based on processor type, the market is fragmented into CPU, GPU, field programmable gate array (FPGA), and application-specific integrated circuit (ASIAC).

The GPU segment dominates due to its excellent parallel processing capabilities, it is well-suited for applications in AI, machine learning, and high-performance computing. GPUs have become the preferred choice for data-heavy tasks in fields such as AI and deep learning due to their capacity for handling extensive parallel workloads. Although CPUs are still the most common choice for general computing tasks and FPGAs and ASICs are preferred for specific functions, GPUs have emerged as the top choice in the market due to its flexibility and scalability. Additionally recent innovations in the industry support this trend. For instance,

• In 2023, AMD launched its Instinct MI300X AI accelerator, a powerful GPU designed for AI and HPC workloads. With 192GB of HBM3 memory and a memory bandwidth of 5.3 TB/s, the MI300X is built to handle demanding AI training and inference tasks.

Analysis by Accelerator Type

Based on accelerator type, the market is fragmented into cloud accelerator and high-performance computing accelerator.

The cloud accelerator segment dominates the market due to the increasing demand for cloud-based services that require high computational power for tasks such as AI and machine learning. Cloud accelerators offer businesses the ability to process large amounts of data and perform complex calculations without the need for costly on-premises hardware, owing to their scalable and on-demand processing capabilities. With the increasing number of companies migrating their operations to the cloud and looking for effective, adaptable data processing solutions, the demand for cloud accelerators has surged, making them a crucial factor in the market. The increasing use of AI in different sectors and improvements in cloud infrastructure are driving the need for more advanced and effective cloud computing solutions. For instance,

• In 2024,AMD and IBM partnered to deploy Instinct MI300X accelerators as a service on IBM Cloud to boost cloud acceleration for AI models and high-performance computing. This collaboration aims to enhance performance and energy efficiency, supporting IBM's watsonx AI and Red Hat Linux AI inferencing.

Analysis by Application

Based on application, the market is subdivided into machine learning, video and image processing, data analytics, and others (financial computing).

The machine learning segment dominates the accelerator card market as it requires immense computational power to train and infer large, complex models, which accelerators such as GPUs are uniquely suited for. The increasing need for strong hardware to quickly and efficiently process large amounts of data in various industries, such as healthcare and finance, is a result of the rising demand for artificial intelligence (AI).

GPUs, especially ones tailored for machine learning, offer the parallel processing power needed to speed up these calculations. Additionally, the recent launch of products in the industry supports this trend. For instance,

• In 2024,Cloudera launched Accelerators for Machine Learning Projects (AMPs) to fast-track AI deployments. These pre-built, open-source solutions, featuring tools such as Fine-Tuning Studio and RAG with Knowledge Graph, enable quick AI adoption and maximize data value. Cloudera showcased them at EVOLVE24 in Dubai, highlighting their impact on accelerating AI projects and enhancing productivity.

Regional Analysis

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Based on region, the market has been studied across North America, Europe, Asia Pacific, South America, and the Middle East & Africa.

North America holds a majority share in the accelerator card market. The region is where leading technology companies such as NVIDIA, AMD, and Intel are located, leading the way in accelerator card technology advancements, especially for AI, machine learning, and high-performance computing. Furthermore, there is a significant need for AI and data-driven solutions in various industries such as healthcare, finance, and automotive in North America, leading to an increase in the use of accelerator cards for quicker data processing. Moreover, the dominance of North America in the market is supported by robust enterprise adoption, academic research, and recent innovations. For instance,

• In 2024, IBM unveiled the Spyre accelerator at Hot Chips 2024, boosting IBM Z’s AI capabilities with 32 cores and PCIe card clustering. It enables efficient generative AI tasks, fraud detection, and on-premises AI model fine-tuning while ensuring secure and scalable enterprise AI solutions.

Europe holds the second-largest share in the accelerator card market, driven by significant investments in AI research and digital transformation across key industries. Germany, France, and the U.K. are at the forefront, with industries such as automotive, healthcare, and finance increasingly turning to AI technology. Industries striving for progress in areas such as autonomous driving, smart manufacturing, and precision medicine have a strong demand for accelerator cards. Moreover, Europe's emphasis on sustainability and innovation, along with government-supported programs promoting AI utilization, heightens the demand for effective computing solutions, highlighting the importance of accelerator cards in the region's technology-driven advancement.

Asia Pacific holds the third-largest share in the global accelerator card market, fueled by the region's rapid technological advancements and strong presence of leading semiconductor manufacturers, such as Japan, South Korea, and Taiwan. The rise in the usage of AI technologies in sectors such as electronics, e-commerce, and telecommunications is feeling the need for accelerator cards. Furthermore, China and India are making significant investments in AI projects, specifically focusing on areas such as manufacturing, fintech, and healthcare, which is increasing the demand for advanced computing solutions. The increasing emphasis on digitalization and innovation in developing markets in the Asia-Pacific region positions it as a crucial player in the accelerator card market. For instance,

• In 2023, Intel launched the Habana Gaudi 2 deep learning accelerator card for the Chinese market, featuring 24 Tensor Processing Cores, 96GB HBM2E memory, and 2.4TB/s bandwidth. Tailored to local regulations, it supports large-scale AI models, offering improved performance and cost efficiency. Server products using Gaudi 2 are expected from Inspur, H3C, and XFUSION.

Key Players Covered

The global accelerator card market is fragmented with the presence of a large number of groups and standalone providers. In the U.S., the top 5 players account for around 28% of the market.

The report includes the profiles of the following key players:

• Alphabet Inc (U.S.)
• AMD (U.S.)
• Achronix Semiconductor Corporation (U.S.)
• Dell Technologies (U.S.)
• Hewlett Packard Enterprise (U.S.)
• IBM (U.S.)
• Intel (U.S.)
• Nvidia (U.S.)
• Qualcomm (U.S.)

Key Industry Developments

• In 2024, AMD launched the Alveo UL3422 accelerator card, a slim, cost-effective solution optimized for ultra-low latency electronic trading. Powered by the AMD Virtex UltraScale+ FPGA, it achieves industry-leading tick-to-trade performance with under 3ns latency, catering to trading firms of all sizes.

• In 2024, Dell’s PowerEdge XE9680 integrates the Intel Gaudi 3 AI accelerator card, delivering cost-efficient, high-performance AI computing. Offering up to 1.8x better compute performance and faster training/inference speeds, it leverages Ethernet networking for scalability and affordability.