Data Center Accelerator Market to Reach USD 372.68 Billion

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.