Market Executive Summary

AI accelerator is a specialized hardware designed to optimize the processing of artificial intelligence tasks, particularly those involving machine learning, neural networks, and deep learning. AI accelerators have evolved into critical components in the fields of AI research and development, especially for handling massive parallel computations required for tasks like training and running AI models. Unlike general-purpose processors like CPUs, AI accelerators such as GPUs, TPUs, FPGAs, and ASICs are built to efficiently handle the complex mathematical operations that underpin AI algorithms. These accelerators significantly reduce the time it takes to train and deploy AI models, making them indispensable for industries ranging from autonomous vehicles to medical diagnostics, and from finance to natural language processing.

The global market for AI Accelerator was estimated to be worth US$ 13530 million in 2023 and is forecast to a readjusted size of US$ 54760 million by 2030 with a CAGR of 22.6% during the forecast period 2024-2030.

The demand for AI accelerators is growing rapidly as industries increasingly incorporate AI into their operations. Leading companies such as NVIDIA, Google, Intel, AMD, and others have introduced specialized chips aimed at enhancing AI workloads' efficiency. The key advantage of AI accelerators is their ability to perform thousands of simultaneous computations, making them ideal for both training massive AI models and processing real-time inference in critical applications. In this context, AI accelerators are not just an enhancement to current computing systems; they are a fundamental shift in how processing power is delivered for AI-specific tasks.

In addition, the interconnection between AI accelerators is critical for handling large-scale AI workloads. These accelerators must communicate efficiently to distribute workloads across multiple units. Currently, technologies like PCIe and CXL (Compute Express Link) facilitate communication between processors and accelerators, but as workloads scale, faster, more specialized interconnects are needed.

Looking forward, one of the most anticipated developments is UALink (Ultra Accelerator Link), a new standard that promises to revolutionize how AI accelerators communicate within servers. UALink aims to create faster and more efficient communication between AI accelerator chips in servers, addressing the challenges of data transfer speed and latency in massive AI workloads. 

The AI Accelerator market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI Accelerator.

Global AI Accelerator Companies Covered

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

Global AI Accelerator Market, by Region

North America (U.S., Canada, Mexico)

Europe (Germany, France, UK, Italy, etc.)

Asia Pacific (China, Japan, South Korea, Southeast Asia, India, etc.)

South America (Brazil, etc.)

Middle East and Africa (Turkey, GCC Countries, Africa, etc.)

Global AI Accelerator Market, Segment by Type

Graphics Processing Unit (GPU)

Vision Processing Unit (VPU)

Others

Global AI Accelerator Market, Segment by Application

Robotics

Consumer Electronics

Security Systems

Others

Chapter Outline

Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.

Chapter 2: Detailed analysis of AI Accelerator company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.

Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.

Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.

Chapter 5: Revenue of AI Accelerator in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.

Chapter 6: Revenue of AI Accelerator in country level. It provides sigmate data by Type, and by Application for each country/region.

Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.

Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 9: Conclusion.