Market Insight-Global Data Center Accelerator Market Overview 2024
Global Data Center Accelerator Market Was Valued at USD 83.26 Billion in 2023 and is Expected to Reach USD 704.58 Billion by the End of 2030, Growing at a CAGR of 30.93% Between 2024 and 2030.– Bossonresearch.com
A data center accelerator is a specialized hardware component designed to enhance computing performance in data centers, particularly in high-performance computing (HPC), artificial intelligence (AI), machine learning, and large-scale data processing tasks. These accelerators offload specific resource-intensive tasks from traditional central processing units (CPUs), allowing complex operations to be performed more efficiently. Types of data center accelerators include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and CPUs, which can be customized for specific workloads. These devices significantly improve the speed and efficiency of data centers by enhancing parallel processing, reducing latency, and increasing throughput, which is critical for modern applications such as AI model training, big data analytics, and cloud services.
The data center accelerator market is undergoing a major transformation driven by multiple development trends, and customized hardware, AI integration, and strategic data center expansion will dominate the accelerator and data center landscape in the future. The rise of self-developed chips by major cloud service providers such as Google, Amazon, Alibaba, and Microsoft. These companies are designing their own CPUs and AI acceleration chips to meet their specific needs, helping them optimize performance, reduce costs, and reduce reliance on traditional vendors such as Intel and AMD. AI accelerators such as GPUs, FPGAs, and ASICs are essential to meeting the parallel computing needs of advanced AI workloads, significantly improving computational efficiency for tasks such as training large language models.
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The global Data Center Accelerator market was valued at USD 83.26 billion in 2023 and is expected to reach USD 704.58 billion by the end of 2030, growing at a CAGR of 30.93% between 2024 and 2030. The data center accelerator market is driven by several key factors. The urgent need for fast and efficient data processing has led to increasing adoption of accelerators such as GPUs, FPGAs, and ASICs. These technologies help process increasingly complex data volumes from sources such as IoT devices and AI workloads. Second, the growing demand for cloud services has prompted cloud providers to use accelerators to improve performance and scalability, especially as hybrid and multi-cloud strategies become more common. In addition, AI investments have accelerated due to the rise of generative AI services, spurring large-scale infrastructure upgrades, including AI-specific hardware. Another key force is the emphasis on energy efficiency and sustainability, with accelerators helping to reduce power consumption in data centers, which is critical given the industry's high energy demands. Finally, the advent of 5G and edge computing is driving data centers to process data in real time, increasing reliance on low-latency accelerators to support emerging technologies such as autonomous systems and the Internet of Things.
Figure Global Data Center Accelerator Market Size (M USD)
Source: Bossonresearch.com, 2024
Driving Factors
Urgent Need for Fast and Efficient Data Processing
The rapid growth of data volume has greatly promoted the development and adoption of data center accelerators, such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs).
These accelerators are designed to handle specialized and intensive computing tasks more efficiently than traditional central processing units (CPUs). The large amount of data generated by social media platforms, IoT devices, online transactions, video streaming, and various other sources requires the processing power provided by these accelerators.
The amount and complexity of data require powerful computing power to perform real-time data analysis, machine learning (ML), and artificial intelligence (AI) tasks. For example, GPUs are very efficient in parallel processing, which is essential for training deep learning (DL) models. This parallelism can process large data sets faster, resulting in faster insights and decision-making capabilities. As AI and machine learning applications become more and more common, the demand for GPUs in data centers continues to increase, driving the advancement of GPU technology and its integration with data center infrastructure.
In addition, the demand for low-latency and high-throughput data processing in modern applications is another key factor. Real-time applications such as self-driving cars, financial transactions, and medical diagnostics rely on fast data processing and instant response times. FPGAs and ASICs are designed for real-time applications because they can be customized for specific tasks and have lower latency and higher efficiency than general-purpose CPUs. These dedicated hardware solutions enable data centers to meet the needs of modern applications and services by providing high throughput, minimal latency, scalability, and energy efficiency.
In addition, the emergence of edge computing brings computing and storage to more important locations, which further drives the demand for efficient data center accelerators.
Growing Demand for Cloud Services
The rapid expansion of cloud services has led to a surge in data center deployments, and vendors hope to differentiate their products by improving performance and efficiency through accelerators. The global migration to cloud infrastructure has driven the demand for data center accelerators in different vertical industries. Cloud service providers (CSPs) such as AWS, Google Cloud, and Microsoft Azure are rapidly expanding their infrastructure to meet the growing demand for efficient and scalable computing. Accelerators enable these CSPs to improve performance and energy efficiency, helping their services stand out. This trend is expected to continue as more enterprises adopt hybrid and multi-cloud strategies. As cloud-based AI workloads surge, these data centers will rely heavily on accelerators to handle machine learning, data analysis, and reasoning tasks. In addition, serverless computing and containers are becoming more prominent in cloud architectures, further increasing the load on accelerators as users move to more scalable on-demand computing environments.
Market Investment in AI Data Centers is Accelerating
The rise of AI, especially after the success of services such as ChatGPT, has sparked a renewed focus and accelerated investment in data center infrastructure. The focus is on specialized hardware for AI and machine learning workloads, such as NVIDIA's GPUs, Google's TPUs, and other AI-specific accelerators. This shift is driven by the exponential growth of generative AI (GenAI) services, whose applications include natural language processing, image recognition, and autonomous systems.
Because AI workloads require a lot of resources, the surge in AI investment extends to infrastructure components such as networks, storage, and cooling systems. Cooling systems in particular have made progress to accommodate the increased power consumption and heat generated by accelerators. Additionally, land purchases in high-demand markets such as Northern Virginia and Phoenix highlight a growing trend where data center operators anticipate further growth in AI-driven cloud computing.
To put this into perspective, while AI is not the only reason for data center expansion, the correlation between AI demand and infrastructure investment is undeniable. The race by hyperscale cloud providers to build AI-driven infrastructure is a key indicator of future market growth.
Energy Efficiency and Sustainability Issues
Energy consumption in data centers is a growing concern, especially as demand for computing power continues to increase. Global data centers reportedly consume nearly 1% of the world's electricity, a proportion that is expected to rise as AI, IoT, and cloud services expand. Energy efficiency is a key factor in reducing operating costs, minimizing environmental impact, and achieving sustainability goals.
Data center accelerators, such as GPUs, FPGAs, and ASICs, play a key role in optimizing energy usage while providing high-performance computing. Compared to traditional CPUs, these accelerators are more efficient when performing specific tasks (e.g., AI model training, large-scale simulations), resulting in significant energy savings. For example, GPUs have advanced power management features that can optimize energy usage during heavy computing tasks. FPGAs can be customized to balance power efficiency and performance, especially in real-time and mission-critical applications.
In addition, data centers are adopting liquid cooling systems and AI-driven energy management tools to reduce the heat generated by accelerators, further reducing their energy footprint. Companies such as Google and Facebook are investing in sustainability projects aimed at using 100% renewable energy for their data centers. These initiatives highlight the role of energy-efficient accelerators in achieving business and environmental goals.
The Advent of 5G And Edge Computing
The rollout of 5G networks and the growth of edge computing have created new demands for data centers to process large amounts of data in real time. 5G networks promise higher bandwidth, lower latency, and greater connectivity, driving the rise of IoT devices, autonomous systems, and smart cities. As more data is generated and transmitted in real time, data centers increasingly need to adopt accelerators that can efficiently manage and analyze this information at the edge.
Edge data centers are closer to the source of data generation (e.g., smart devices, sensors) and require accelerators that can provide low-latency, high-performance computing. FPGAs and ASICs are particularly useful in edge computing because they can be customized for specific real-time tasks such as video streaming, AI inference, and IoT data processing. In addition, GPUs are also used in edge AI applications that require heavy computation at the device level. The increasing adoption of AI at the edge and in 5G infrastructure is expected to drive the demand for data center accelerators.
Key Development Trends
Self-developed chips by cloud service providers
For leading cloud service providers, facing their own huge demand for data center CPUs and AI acceleration chips, as well as their own specific software stacks and application requirements, self-developed customized chips can not only provide higher energy efficiency performance, but also reduce chip outsourcing costs and energy consumption costs, and reduce dependence on a few chip suppliers.
Therefore, we can see that many cloud service providers such as Google, Amazon, Alibaba, Huawei, Baidu, and Microsoft have launched self-developed data center CPUs and AI acceleration chips in recent years.
In addition to Google's Axion CPU and TPU for data centers, Amazon also has its own Graviton CPU for data centers and cloud AI chips called Trainium and Inferentia, which are trying to reduce the cost of using various cloud computing power and services for customers as much as possible.
Alibaba also launched its self-developed data center AI acceleration chip Hanguang 800 a long time ago, and then launched its self-developed Arm architecture data center CPU-Yitian 710. At present, these self-developed chips are mainly used in Alibaba's internal services and related instances of cloud services.
In addition, Huawei also has its own Arm-based data center CPU Kunpeng 920 series, as well as data center AI acceleration chip Ascend 910 series. Baidu also launched its own data center AI acceleration chip Kunlun core early on, and the business has been spun off and supplied to the outside world.
Microsoft has also launched its own data center processor Cobalt 100 and data center AI accelerator Maia 100. Although Microsoft's AI infrastructure currently still relies on Intel CPUs and Nvidia GPUs, Microsoft has begun to gradually adjust its software stack to self-developed chips.
Currently, all major cloud providers and hyperscale providers are developing self-developed data center processors to replace chips produced by Intel and AMD. However, Intel and AMD still dominate the data center CPU market, while Nvidia has an absolute monopoly in the data center GPU field and forces cloud providers to allocate dedicated spaces controlled by Nvidia, where Nvidia places its DGX servers and CUDA software stacks.
Expansion of the AI accelerator market
Mastering AI technology has an increasingly prominent impact on the economy, society, energy, military and geopolitical landscape. The widespread application of advanced AI technology in enterprises, governments, and individual institutions is not only strategic but also imperative.
Although much research on AI in the past seventy years has mostly failed to achieve the expected success, AI technology has made significant progress in the past decade, and its development speed has increased exponentially. The rapid development of AI has been driven by the shift to highly parallel computing architectures, which are different from traditional central processing unit (CPU)-based systems. Due to the sequential processing characteristics, traditional CPUs can only process one instruction at a time, and are increasingly unable to meet the needs of advanced, highly parallel AI algorithms, such as large language models (LLM). This challenge has driven the widespread development of AI accelerators, which can significantly improve the performance of AI applications.
Chiplet
The development of chiplet technology is transforming the data center accelerator market by offering a scalable, cost-effective, and sustainable path forward for chip development. As data center demands grow, particularly in AI and HPC applications, chiplets are becoming an essential technology to address performance needs while controlling costs. This trend is expected to intensify in the coming years as companies continue to push the boundaries of computing power and efficiency.
With the development of the latest process technology, chip design costs have increased to prohibitive levels. According to IBS estimates, the total cost of developing 2nm chips from scratch will reach $725 million. Using Chiplets can significantly reduce chip development time and costs: you only need to update key modules and you have a brand new chip. From a cost perspective, in the near future, as manufacturing processes continue to improve, it will be almost impossible to build leading chips without using Chiplets.
Total cost of ownership (TCO) is one of the main constraints in bringing models into production in the data center, and the cost of volume production of chips is a significant component of TCO. As the scale of data centers continues to expand, the impact on TCO will also become greater. Tirias Research predicts that by 2028, the server infrastructure + operating costs of a typical GenAI data center will exceed $76 billion. Chiplets allow developers to choose different processes for each module, flexibly balancing performance and cost, without having to bet all functions on expensive and difficult-to-obtain cutting-edge processes.
Under the constraints of Moore's Law and light size, chiplets have become an economical and sustainable way to continue to enhance chip performance. Through 2.5D tiling/3D stacked core particles, chiplets can effectively expand chip performance and increase chip complexity. Of course, this also brings about interconnection problems. After all, if these cores cannot be effectively connected together, nothing can be done.
Another key benefit of Chiplets is the reduced time to market (TTM) of developing chips. By hiding complex functionality in reusable, proven chips, companies can effectively shorten the time to market required to develop custom new chips and accelerate the development and innovation of next-generation products.
Giants compete to plan for future demand
In recent years, technology companies have flocked to Northern Virginia to rent data centers, and secondary market demand in central Ohio has also risen. Amazon has invested $7.8 billion to build data centers in central Ohio. Moreover, with the popularity of remote work, a large amount of funds have also flowed into data centers, communication infrastructure, optical fiber, signal towers and related technology industries.
Relevant data show that there are currently more than 8,000 data centers in the world, mainly distributed in the United States, Asia and Europe. Northern Virginia in the United States is the world's largest data center distribution center, with about 300 data centers. From the perspective of energy consumption, in 2023, Northern Virginia data centers will consume 2552MW of electricity, Dallas will consume 654MW, Silicon Valley will consume 615MW, Beijing data centers will consume 1799MW, London will consume 1052MW, and Frankfurt will consume 864MW.
AI takes 10 years or even longer to build. The current global data center capacity is about 50GW, which will increase to 100GW in the next 6-10 years. And data centers have become AI factories, inputting data and outputting intelligence and insights. It is reported that Meta, Microsoft, Google, Amazon, Oracle, and Akamai are all very active in building and expanding their own data centers.
It is reported that Meta is preparing to invest $800 million to build a data center in Jeffersonville, Indiana, USA. This is Meta's 18th data center built in the United States and the 22nd data center in the world. Microsoft will invest $3.4 billion in Germany, mainly in AI infrastructure and cloud computing facilities. Alphabet is preparing to expand its data center infrastructure, with the largest project located in Waltham Cross, UK.
The market generally believes that generative AI is impacting the hardware market and forcing companies to redesign data centers. Due to the exponential growth of AI research, the demand for AI computing will increase massively in the next 10 years. It can be expected that there will be a significant change in global data centers in 2024, and integrating AI into Ops and deploying GPUs on a large scale are the direction of change. This is a trend, because the computing power required by the industry is increasing, and sustainable operations are becoming more and more important. To meet the demand, it is necessary to build its own huge computing center and data center. The layout of the giants is a plan for the future.
Cloud vendors will remain the largest customers of leased data centers
Despite concerns that cloud vendors will reduce data center leasing by building their own data centers or reducing infrastructure growth, large public cloud and IT companies will continue to use leased data center space, accounting for the majority of leased data center demand in many regions. In part due to the expected demand for AI infrastructure in public clouds, cloud vendors are accelerating the construction of their own facilities and leasing more data center capacity at a record pace. Typically, they will build their own data center facilities in markets where they have been operating for many years, while also leasing in these places. Public cloud vendors will also continue to expand into emerging markets, in countries where they have not previously deployed data centers, which will change the local data center industry. The main regions where cloud demand has increased include Southeast Asia (markets such as Indonesia, the Philippines, Thailand and Vietnam), Africa, Latin America, India, the Middle East and Eastern Europe. Even major markets such as Northern Virginia, Phoenix, Dallas, Tokyo, Japan and Frankfurt, Germany are experiencing record demand for capacity amid rising power density.
Business Operations and Ownership Models
Real estate funds and other real estate investors have entered and expanded their presence in the data center sector. This has increased competition in certain regions, where data center service providers can offer predictable sales revenue opportunities that attract investors, while freeing up capital for data center service providers to build new data center facilities in regions that may be more risky but have growth potential. Business models are evolving, with parties becoming more specialized in managing capital, designing, building and operating facilities, and serving specific customer segments.
AI Elevates Data Center Operations
Enhanced data center management software and AI-enabled data center infrastructure management (DCIM) are able to analyze as much of the data center's own data as possible to determine when a component may fail or how to improve a facility's energy consumption. As AI systems become better trained and this approach is validated in a variety of scenarios, it will become more widespread and enable more data center equipment management to be automated. However, many data center service providers currently use homegrown systems or systems that combine multiple off-the-shelf components in a proprietary way. This may delay the application of more advanced AI systems. So far, in some cases, the challenges of installing new systems seem to outweigh the benefits of new systems. However, the use of more advanced AI software that makes data centers easier to manage may soon differentiate some data center service providers and lead to wider adoption in the industry. We will continue to analyze the need for data center management software, industry innovation, adoption of new systems, and barriers to widespread use.
The Positive and Negative Impacts of Blockchain and Cryptocurrency
The cryptocurrency mining industry continues to grow and is increasing the demand for data center infrastructure. Most mining companies build and operate their own data centers, and some are considering providing these services to other companies, especially companies with similar computing models and requirements, such as those with HPC and AI workloads. To date, providers that have moved in this direction have built facilities that are completely different from mining activities for expansion, facilitating greater investment in infrastructure, protecting investment goals, and targeting different customers. Regardless, we believe that cryptocurrency companies are likely to become competitors to traditional rental data center service providers, both targeting higher density deployment types. Some cryptocurrency companies still lease data center space, which has driven the development of the data center industry in areas with relatively low electricity costs. We will continue to monitor the development of the blockchain and cryptocurrency industry, its requirements, and the impact on the data center industry.
Global Data Center Accelerator Market: Competitive Landscape
According to estimates, the concentration indicators of the Data Center Accelerator market in 2023, CR5 and HHI, are 78.33% and 25.48%, respectively, highlighting a high concentration. The top five companies dominate the market, influencing pricing, technology trends, and product availability. Major companies such as NVIDIA, Intel, AMD, etc. have established considerable market share through continuous innovation, extensive R&D, and strategic partnerships, solidifying their position in the competitive environment. Although the market is concentrated, it is not monopolized, allowing for some competition among a wider range of players, especially in emerging markets where demand for data center accelerators is surging. This competitive but concentrated environment affects the speed of adoption of new technologies and the operational efficiency of data centers in meeting growing consumer and enterprise demands. Currently, major players in the market include Nvidia Corporation, Intel Corporation, Advanced Micro Devices, Inc., Broadcom Inc., Marvell Technology Group Ltd., Enflame Technology, Lattice Semiconductor, Gyrfalcon Technology Inc., Achronix Semiconductor Corporation, LeapMind Inc., Graphcore, Others.
Key players in the Data Center Accelerator Market include:
Nvidia Corporation
Intel Corporation
Advanced Micro Devices, Inc.
Broadcom Inc.
Marvell Technology Group Ltd.
Enflame Technology
Lattice Semiconductor
Gyrfalcon Technology Inc.
Achronix Semiconductor Corporation
LeapMind Inc.
Graphcore
Others
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