· The total value of AI accelerators in servers will reach US$21 billion in 2024
· Computing electronics will account for 47% of the total AI chip market revenue
· By the end of 2026, AI PCs will account for 100% of corporate PC purchases
Gartner's latest forecast predicts that global artificial intelligence (AI) semiconductor revenue will reach US$71 billion in 2024, a 33% increase from 2023.
"Generative artificial intelligence (GAI) is currently driving demand for high-performance AI chips in data centers. The total value of AI accelerators (used to offload data processing from microprocessors) used in servers will reach $21 billion in 2024 and will increase to $33 billion by 2028," said Alan Priestley, research vice president at Gartner.
Gartner predicts that by 2024, AI personal computer (PC) shipments will account for 22% of total PC shipments; by the end of 2026, AI PCs will account for 100% of enterprise PC purchases. The neural processing unit (NPU) in AI PCs can extend its operating time, reduce operating noise and temperature, and continuously run AI tasks in the background. This brings new possibilities for the application of AI in daily activities.
AI semiconductor revenue will continue to grow by double digits throughout the forecast period and achieve the highest growth rate in 2024 (see Table 1).
Table 1. Global AI semiconductor revenue forecast for 2023-2025 (Unit: million US dollars)
| 2023 | 2024 | 2025 | |
| Revenue (millions of US dollars) | 53,662 | 71,252 | 91,955 |
Data source (May 2024)
AI chip revenue from computing electronics will account for a record high share of electronic devices
In 2024, AI chip revenue from computer electronics is expected to reach $33.4 billion, accounting for 47% of total AI semiconductor revenue; AI chip revenue from automotive electronics and consumer electronics is expected to reach $7.1 billion and $1.8 billion, respectively.
Semiconductor manufacturers and technology companies are "fighting fiercely"
While most people's attention is focused on using high-performance graphics processors (GPUs) for new AI workloads, major hyperscalers (AWS, Google, Meta and Microsoft) are already investing in developing their own AI-optimized chips. Although chip development is expensive, using customized chips can improve operational efficiency, reduce the cost of providing AI services to users, and reduce the cost of users using new AI applications. "As the market moves from development to deployment, this trend is expected to continue," Priestley said.
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