Artificial Intelligence (AI) Accelerator Market Size, Share, Growth, Analysis, Key Players, Revenue, | Valuates Reports

Artificial Intelligence (AI) Accelerator Market Size

The global Artificial Intelligence (AI) Accelerator market was valued at US$ 11530 million in 2023 and is anticipated to reach US$ 54760 million by 2030, witnessing a CAGR of 22.6% during the forecast period 2024-2030.

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Artificial Intelligence (AI) accelerator is a specialized hardware designed to speed up AI-specific computations, particularly those related to machine learning (ML) and deep learning (DL) tasks. These accelerators are optimized to handle large-scale data processing, matrix operations, and neural network training. Unlike traditional central processing units (CPUs), AI accelerators like graphics processing units (GPUs), tensor processing units (TPUs), and application-specific integrated circuits (ASICs) are designed to perform parallel computations, which significantly enhances their ability to process complex AI models. AI accelerators have become essential components in AI applications such as natural language processing (NLP), autonomous vehicles, robotics, and computer vision, where real-time or near-real-time processing is required.

Artificial Intelligence (AI) Accelerator market has grown significantly over the last few years due to the increasing adoption of AI technologies across multiple industries. Companies such as NVIDIA, AMD, Google, and Intel have been leading the development of these hardware solutions. GPUs, particularly from NVIDIA, are widely regarded as the leading AI accelerators, given their strong parallel processing capabilities. However, newer players like Google with its TPUs and other AI-focused hardware are expanding the market. As AI becomes more integral to areas such as healthcare, finance, autonomous driving, and cloud computing, the demand for accelerators that can handle high-performance, low-latency AI workloads continues to grow.

In 2023, the global Artificial Intelligence (AI) Accelerator market is characterized by intense competition, with companies aiming to optimize power efficiency, speed, and scalability. High-performance computing (HPC) centers, data centers, and cloud service providers are among the largest consumers of AI accelerators, using them to process large datasets and support AI-driven applications. The market is driven by a need for accelerators that can reduce training time for deep learning models, increase inference speeds, and handle more complex algorithms in real-time applications.

The future of AI accelerators is closely tied to the development of new interconnect standards, such as UALink (Ultra Accelerator Link), which promises to improve communication between AI accelerators within data centers. UALink 1.0, expected to be released soon, aims to enable seamless connectivity between up to 1,024 AI accelerator chips (such as GPUs or TPUs), which will be connected to a single ultra-accelerator switch.

In addition to interconnectivity, there is a strong focus on improving the energy efficiency
and form factor of AI accelerators, especially as AI applications move beyond the data center to the edge. Edge computing, where AI accelerators are embedded in devices like smartphones, drones, and autonomous vehicles, requires hardware that is not only powerful but also energy-efficient and compact. As AI technologies become more ubiquitous, the demand for flexible, scalable, and energy-efficient AI accelerators will drive innovation in both hardware and integration techniques.

By Type
• Graphics Processing Units (GPUs)
• Tensor Processing Units (TPUs)
• Field-Programmable Gate Arrays (FPGAs)
• Application-Specific Integrated Circuits (ASICs)
• Neural Processing Units (NPUs)

By Application
• Automotive
• Healthcare
• Manufacturing
• Agriculture
• Cyber Security
• Government

Key Companies
Huawei, Qualcomm, Intel, IBM, Amazon Web Services, NVIDIA, AMD, Achronix, Google (Alphabet), Hailo, Alibaba, Groq, MediaTek, Microsoft, Samsung

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This release was published on openPR.