Accelerator Card Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Processor Type (Central Processing Units (CPU), Graphics Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), Application-specific Integrated Circuit (ASIAC)), By Accelerator Type (High-performance Computing Accelerator, Cloud Accelerator), By Application (Video And Image Processing, Machine Learning, Data Analytics, Mobile Phones, Others), By Region, By Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 6.32 Billion
CAGR (2026-2031)	33.43%
Fastest Growing Segment	Machine Learning
Largest Market	North America
Market Size (2031)	USD 35.66 Billion

Market Overview

The Global Accelerator Card Market will grow from USD 6.32 Billion in 2025 to USD 35.66 Billion by 2031 at a 33.43% CAGR. Global accelerator cards are specialized hardware devices designed to offload heavy processing tasks from the central processing unit to improve overall system efficiency and performance. These components are essential for handling complex workloads such as artificial intelligence, data analytics, and network security functions within enterprise and cloud environments. The market is primarily driven by the escalating demand for massive computing power in data centers and the rapid proliferation of machine learning applications. Additionally, the necessity for minimal latency data processing at the network edge supports the continuous adoption of these dedicated modules.

Despite the robust growth trajectory, the market faces a significant challenge regarding the substantial energy consumption and heat dissipation requirements of advanced hardware, which often necessitate costly infrastructure upgrades. This thermal constraint can limit deployment speeds in legacy facilities. According to SEMI, in 2024, global leading edge semiconductor capacity for 5 nanometer nodes and smaller was projected to increase by 13 percent, a rise driven chiefly by generative AI requirements for data center training and inference. This expansion highlights the critical reliance on the advanced logic utilized in modern accelerator systems.

Key Market Drivers

The exponential growth in AI and machine learning workloads stands as the primary force propelling the accelerator card market. Modern neural networks require parallel processing capabilities that far exceed traditional CPU performance, necessitating the deployment of high-throughput GPUs and specialized ASICs. This demand is particularly acute for training large language models, where computational speed directly correlates with competitive advantage and model accuracy. Consequently, enterprises are heavily investing in hardware that can manage the intensive matrix calculations required for generative AI. According to NVIDIA, August 2024, in the 'NVIDIA Announces Financial Results for Second Quarter Fiscal 2025', Data Center revenue reached a record 26.3 billion dollars, marking a 154 percent increase from the previous year, demonstrating the immense financial scale of this hardware adoption.

Simultaneously, the rapid expansion of hyperscale data centers and cloud infrastructure is creating a sustained requirement for dense computing modules. Cloud service providers are aggressively scaling their physical footprint to offer AI-as-a-service and high-performance computing tiers, driving the integration of accelerator cards into server racks to maximize rack density and energy efficiency per operation. According to Microsoft, July 2024, in the 'Fiscal Year 2024 Fourth Quarter Results', capital expenditures including finance leases were 19 billion dollars to support cloud and AI offerings. This infrastructure build-out broadens the market for diverse hardware solutions beyond a single provider. According to AMD, October 2024, in the 'Third Quarter 2024 Financial Results', the company updated its 2024 data center GPU revenue outlook to exceed 5 billion dollars, reflecting the widening ecosystem of accelerator deployment.

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Key Market Challenges

The substantial energy consumption and heat dissipation requirements of accelerator cards present a notable restraint on market expansion. As these devices are integrated to handle intensive workloads, they generate significant thermal output that necessitates specialized cooling infrastructure. This operational requirement imposes high financial costs on data center operators, particularly those managing legacy facilities that were not designed for such high-density power usage. Consequently, the need for expensive structural retrofits often delays the procurement and installation of new hardware, directly slowing the adoption rate of these performance modules.

This trend of rising power intensity is evident in broader industrial energy patterns which impacts deployment strategies. According to the International Energy Agency, in 2024, global electricity demand from data centers, artificial intelligence, and the cryptocurrency sector was projected to potentially double by 2026 to reach roughly 1,000 terawatt-hours. This projected surge in energy demand underscores the logistical and financial difficulties companies face when scaling operations. The physical limitations regarding power delivery and thermal regulation therefore act as a practical ceiling on how quickly organizations can deploy additional accelerator cards.

Key Market Trends

The integration of Compute Express Link (CXL) technology is fundamentally reshaping the accelerator card market by enabling cache-coherent interconnects between processors and memory devices. This architecture addresses the critical memory wall bottleneck by permitting accelerators to access shared memory pools independent of the host CPU, thereby significantly optimizing data-intensive workloads such as large language model training. The adoption of this standard facilitates disaggregated computing models, allowing data centers to scale memory capacity without a proportional increase in physical server footprint. According to Samsung Electronics, November 2025, in the 'Samsung Highlights Open Collaboration for the AI Era at OCP Global Summit 2025' press release, the company demonstrated its latest CXL memory modules which enable a 50 percent increase in memory capacity and up to a 100 percent improvement in bandwidth for next-generation AI servers compared to traditional configurations.

Simultaneously, there is a marked shift towards Domain-Specific Architectures (DSAs) as hyperscale operators increasingly commission custom silicon to maximize efficiency for specific AI tasks. Unlike general-purpose GPUs, these bespoke accelerators are engineered to strip away unnecessary logic, focusing entirely on the matrix operations required by proprietary neural networks, which significantly reduces the total cost of ownership. This trend has spurred a surge in demand for custom ASIC design and manufacturing services alongside standard hardware procurement. According to Broadcom, September 2025, in the 'Third Quarter Fiscal Year 2025 Financial Results', the company reported a 63 percent year-over-year increase in AI revenue, a surge attributed primarily to the ramping production of custom AI accelerators for hyperscale customers seeking alternatives to standard market offerings.

Segmental Insights

The Machine Learning segment is anticipated to witness the fastest growth in the Global Accelerator Card Market, driven by the expanding reliance on high-performance computing within data centers. Organizations are increasingly adopting these specialized components to process massive datasets and execute complex algorithms essential for artificial intelligence. This trend is accelerated by the need to offload data-intensive tasks from standard processors, thereby improving computational efficiency and speed. Furthermore, the rapid expansion of cloud infrastructure to support automated services reinforces the critical role of accelerator cards in facilitating scalable machine learning operations.

Regional Insights

North America maintains a dominant position in the global accelerator card market, driven by the concentration of key technology developers and extensive data center infrastructure. The region’s leadership is reinforced by strategic government support, including initiatives by the United States Department of Energy to enhance supercomputing capabilities. These programs accelerate the integration of processing hardware essential for artificial intelligence and cloud computing operations. Consequently, the combination of robust public sector funding and widespread commercial adoption establishes the region as the central driver of global market demand.

Recent Developments

• In November 2024, NVIDIA unveiled the H200 NVL, a new PCIe-based AI accelerator card designed for enterprise data centers with lower power limitations and air-cooled rack configurations. This accelerator features significantly increased memory capacity and bandwidth compared to previous generations, allowing companies to fine-tune large language models much more efficiently within hours. The product is targeted at mainstream enterprise environments that require high-performance computing capabilities without the complex infrastructure often needed for liquid-cooled systems. The company confirmed that the accelerator would be supported by major server manufacturers, facilitating broad adoption for diverse AI and high-performance computing workloads.
• In October 2024, Achronix Semiconductor Corporation released the Speedster AC7t800, a mid-range FPGA accelerator device tailored for data-centric applications such as generative AI, smart networking, and 5G/6G infrastructure. This new product delivers exceptional bandwidth and low latency by utilizing a unique 2D network-on-chip architecture and supporting high-speed interfaces like PCIe Gen5 and 400 Gigabit Ethernet. The company stated that the FPGA is specifically engineered to meet the growing throughput demands of modern data centers while maintaining optimal power efficiency. It enables rapid data acquisition and processing, making it an ideal solution for deployment in SmartNICs and data processing units.
• In September 2024, Intel formally launched the Gaudi 3 AI accelerator, positioning it as a cost-effective and high-performance alternative for executing complex generative AI workloads. The new accelerator features substantial improvements in networking capabilities and memory bandwidth compared to its predecessor, supporting open ecosystem standards like Ethernet to allow highly scalable system configurations. Designed to compete directly with established market leaders, the Gaudi 3 is optimized for both training and inference tasks in large language models. The company emphasized that the accelerator offers superior power efficiency and a lower total cost of ownership for data centers running diverse AI models.
• In August 2024, Napatech announced a strategic partnership with SigmaX.AI to optimize datacenter infrastructure for artificial intelligence and big data analytics workloads. The collaboration involves integrating Napatech’s programmable Smart Network Interface Cards (SmartNICs) with SigmaX.AI’s high-performance data management software. This joint effort aims to address the data overload challenges in cloud and edge computing environments by significantly enhancing storage and network efficiency. By offloading complex processing tasks to the specialized accelerator cards, the combined solution ensures faster data throughput, reduced latency, and improved energy efficiency for demanding AI applications deployed in enterprise and telecom data centers.

Key Market Players

• NVIDIA Corporation
• Advanced Micro Devices, Inc.
• Intel Corporation
• Alphabet Inc.
• IBM Corporation
• Hewlett Packard Enterprise Development LP
• Dell Technologies Inc.
• Fujitsu Limited
• Cisco Systems, Inc.
• Huawei Technologies Co., Ltd

By Processor Type	By Accelerator Type	By Application	By Region
Central Processing Units (CPU) Graphics Processing Units (GPU) Field-Programmable Gate Arrays (FPGA) Application-specific Integrated Circuit (ASIAC)	High-performance Computing Accelerator Cloud Accelerator	Video And Image Processing Machine Learning Data Analytics Mobile Phones Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Accelerator Card Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Accelerator Card Market, By Processor Type:

• Central Processing Units (CPU)
• Graphics Processing Units (GPU)
• Field-Programmable Gate Arrays (FPGA)
• Application-specific Integrated Circuit (ASIAC)

• Accelerator Card Market, By Accelerator Type:

• High-performance Computing Accelerator
• Cloud Accelerator

• Accelerator Card Market, By Application:

• Video And Image Processing
• Machine Learning
• Data Analytics
• Mobile Phones
• Others

• Accelerator Card Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE