Delray Beach, FL, Sept. 21, 2026 (GLOBE NEWSWIRE) -- The global Data Center Accelerator Market was valued at USD 170.81 billion in 2025 and is projected to reach USD 372.68 billion by 2030, growing at a CAGR of 16.9% from 2025 to 2030, according to a new report by MarketsandMarkets™. The rapid adoption of artificial intelligence, machine learning, and high-performance computing is a key factor driving the market, as technological advancements such as next-generation GPUs, FPGAs, and ASICs, along with optimized hardware-software integration, enhance processing efficiency, scalability, and performance across cloud, enterprise, and edge data centers.
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Key Market Highlights
- Market size, 2025: USD 170.81 Billion
- Market forecast, 2030: USD 372.68 Billion
- Growth rate: CAGR of 16.9% from 2025 to 2030
- Largest region: North America
- Leading data center type: Cloud
- Fastest-growing data center type: Enterprise
- Report scope: 120 market data tables, 70 figures, 250 pages
- Key players: NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), Alphabet, Inc. (US), Amazon Web Services, Inc. (US), Qualcomm Technologies, Inc. (US), Marvell (US), Achronix Semiconductor Corporation (US), Broadcom (US), and Graphcore, Ltd. (UK).
Why This Market Matters
Behind every large language model response, every AI image generated, and every recommendation engine running in the background sits a piece of specialized hardware doing the heavy computational lifting that general-purpose processors simply can't handle fast enough. Data center accelerators — GPUs, ASICs, FPGAs, and custom AI chips — are what let hyperscalers and enterprises train and run increasingly complex AI models without hitting a performance or power wall. As generative AI adoption accelerates across nearly every industry, from healthcare imaging to financial fraud detection to autonomous vehicles, the availability and efficiency of these accelerators has become one of the defining constraints on how fast AI capability can actually be deployed at scale. That makes this market a direct barometer of the AI economy's real-world growth ceiling.
Market Overview
Data center accelerators are specialized hardware modules that enhance CPUs, offering higher throughput, lower latency, and improved energy efficiency for AI training and inference, high-performance computing (HPC), analytics, and infrastructure offload. Key types include GPUs, CPUs, FPGAs, and ASICs. The market is evaluated across data center types, functions, processor types, verticals, and regions, with forecasts supported by vendor strategies, hardware-software integration, performance-per-watt improvements, and supply chain factors. The market is segmented by processor type (GPU, CPU, ASIC, FPGA), function (training, inference), data center type (cloud, enterprise), and vertical (IT & telecom, healthcare, BFSI, government, energy, automotive, retail & e-commerce, and other verticals), with the report covering North America, Europe, Asia Pacific, and the Rest of the World across 17 countries.
Analyst Perspective
According to MarketsandMarkets™, the rise in generative AI workloads is the primary driver of the market, as accelerators such as GPUs, ASICs, and TPUs are critical in handling complex AI training and inference tasks, delivering the high-speed processing and scalability required for models such as large language models, with hyperscalers and enterprises reshaping data center investments globally to prioritize accelerator-driven systems to maintain competitiveness in the AI economy. Analysts see the ability of FPGA and custom silicon accelerators to unlock high-performance, energy-efficient AI deployment as a major opportunity, as unlike general-purpose GPUs, these accelerators can be tailored for specific AI workloads, delivering superior performance and energy efficiency, with their flexibility allowing enterprises to optimize deep learning, edge AI, and inference applications. At the same time, high total cost of ownership remains a significant restraint, as deploying accelerators involves substantial capital outlay not only for hardware but also for associated energy, cooling, and maintenance requirements, making scalability difficult particularly for smaller enterprises where operational expenses often outweigh perceived benefits. Power and cooling inefficiencies constraining scalable deployment are also flagged as a key challenge, as rising computational throughput demands from AI training models increase the risk of overheating and energy inefficiencies, inflating operational costs and prompting the need for innovation in liquid cooling, thermal management, and energy-optimized designs.
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Segment Analysis
By Processor Type: GPUs are anticipated to account for the largest share of the market in 2030, owing to their widespread use in deep learning, high-performance computing, and AI training, offering high memory bandwidth, low-latency computation, and scalable parallel processing capabilities. ASICs are expected to register the highest CAGR of 29.2%, driven by their application-specific design advantages, including energy efficiency, speed, and optimization for tasks such as AI inference.
By Function: Inference is expected to hold the largest share and grow the fastest during the forecast period, driven by the rising deployment of AI-powered services; inference workloads require rapid, low-latency data processing, making accelerators essential for real-time applications such as recommendation engines, natural language processing, and autonomous systems.
By Data Center Type: Cloud data centers are expected to hold the largest share in 2030, supported by hyperscaler investments and growing demand for AI-based workloads across global enterprises. Enterprise data centers are projected to grow at the fastest rate, as organizations increasingly adopt hybrid and edge computing strategies, driven by low-latency, high-security, and regulatory compliance requirements.
By Vertical: The IT & telecom sector is projected to dominate the market in 2030, as global operators invest heavily in 5G, cloud, and AI infrastructure. The automotive industry is expected to grow at the fastest pace, fueled by increasing reliance on AI for autonomous driving, vehicle connectivity, and smart mobility solutions.
Regional Analysis
North America accounted for a 47.0% revenue share of the global data center accelerator market in 2024, the largest of any region, driven by strong investments from hyperscale cloud providers and AI infrastructure developers, with rising deployment of cloud computing technologies to scale IT operations further reinforcing the region's leadership. Asia Pacific is projected to be the fastest-growing region during the forecast period, fueled by rapid cloud adoption, hyperscale data center expansion, and increasing AI-driven workloads, with strong government support, 5G rollouts, and rising digital transformation initiatives across China, India, and Japan further accelerating demand for advanced high-performance computing infrastructure; India is expected to register the highest CAGR in the global market during the forecast period, supported by rapid AI adoption and hyperscale investments. Europe and the Rest of the World also contribute to global demand, supported by expanding cloud and colocation services, AI infrastructure expansion, sovereign compute initiatives, and rising demand for sustainable, energy-efficient data centers across these regions.
Key Industry Trends
- The rise in generative AI workloads continues to be the strongest driver of accelerator demand, pushing hyperscalers and enterprises to expand infrastructure.
- Scaling machine learning via cloud-based accelerators for enterprise data centers is broadening adoption beyond hyperscale environments.
- The shift from general-purpose accelerators toward AI-specific processors — including NPUs, TPUs, DPUs, and IPUs — is reshaping how advanced AI/ML deployments are handled.
- The disruption of edge computing is demanding specialized accelerators for real-time analytics and IoT across industry verticals.
- Deployment of accelerator-as-a-service and subscription-based models is enabling more scalable AI deployment for organizations of all sizes.
- High total cost of ownership and supply chain and packaging constraints remain key restraints limiting broader accelerator adoption, particularly for smaller enterprises.
- Power and cooling inefficiencies, along with architectural fragmentation and software integration complexities, continue to challenge scalable deployment of AI data center accelerators.
Competitive Landscape
MarketsandMarkets™ identifies NVIDIA Corporation, Advanced Micro Devices, and Intel Corporation as star players in the Data Center Accelerator Market, given their strong market share and product footprint. In the company evaluation matrix, NVIDIA Corporation is positioned as a Star, leading with a dominant market presence and an extensive GPU portfolio — including its H100, H200, and Blackwell architectures — driving widespread adoption across cloud, AI, and HPC workloads through exceptional AI performance, CUDA ecosystem dominance, and a comprehensive software stack. Qualcomm Technologies, Inc. is recognized as an Emerging Leader, gaining traction with custom AI and edge accelerator solutions, positioning itself as a growing contender in high-performance computing and enterprise AI applications.
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