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Gartner: Global AI Chips Revenue to Total £56BN in 2024

Thom Carter

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gartner global ai chips revenue to total 55bn in 2024
Additionally, the research and consulting firm predicts that AI PC shipments will reach 22% of the total PC shipments in 2024, and that by the end of 2026, 100% of enterprise PC purchases will be an AI PC. 

Gartner, the technological research and consulting firm, has forecast that the revenue from AI semiconductors globally will total £56 billion ($71.2bn~) in 2024, marking a 33% increase from 2023.

While AI semiconductor revenue is expected to continue to experience double-digit growth through the forecast period of 2023-2025, 2024 is set to experience the highest growth rate during that period.

What’ll Contribute to Global AI Chips Revenue Growth?

AI chips revenue from compute electronics is projected to total £26bn ($33.4bn~) this year, which will account for 47% of total AI semiconductors revenue.

Meanwhile, AI Chips revenue from automotive electronics is expected to reach £5.5bn ($7.1bn~), and £1.4bn (£$1.8bn~) from consumer electronics in 2024.

Also set to grow is AI chips used in data centres. As Alan Priestly, VP analyst at Gartner, explained: “In 2024, the value of AI accelerators used in servers, which offload data processing from microprocessors, will total $21 billion, and increase to $33 billion by 2028.”

Additionally, the research and consulting firm predicts that AI PC shipments will reach 22% of the total PC shipments in 2024, and that by the end of 2026, 100% of enterprise PC purchases will be an AI PC.

AI PCs include a neural processing unit (NPU) which can enable AI PCs to run longer, quieter, and cooler, and have AI tasks running continuously in the background, creating more potential for AI to be leveraged in everyday activities.


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Gartner noted that, while much of the focus is on the use of high-performance graphics processing units (GPUs) for new AI workloads, the major hyperscalers—AWS, Google, Meta, and Microsoft—are all investing in developing their own chips optimised for AI.

While chip development is expected, using custom designed chips can improve operational efficiencies, reduce the costs of delivering AI-based services to users, and lower costs for users to access new AI-based applications.

“As the market shifts from development to deployment we expect to see this trend continue,” added Priestly.

Thom Carter

Staff Writer, DIGIT

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