Gartner forecasts worldwide AI chips revenue to grow 33% to $71bn in 2024

 Image-Gartner-MediaBrief.jpg

Revenue from AI semiconductors globally is expected to total $71 billion in 2024, an increase of 33% from 2023, according to the latest forecast from Gartner, Inc.

Alan Priestley, VP Analyst at Gartner, said, “Today, generative AI (GenAI) is fueling demand for high-performance AI chips in data centers. 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.”


Tune in to NDTV to Understand and get the answer to India's favourite question #ResultKyaRaha.


Gartner forecasts AI PC shipments will reach 22% of the total PC shipments in 2024, and by the end of 2026, 100% of enterprise PC purchases will be an AI PC. AI PCs include a neural processing unit (NPU) enabling AI PCs to run longer, quieter, and cooler and have AI tasks running continually in the background, creating new opportunities for leveraging AI in everyday activities.

While AI semiconductor revenue will continue to experience double-digit growth through the forecast period, 2024 will experience a growth rate during that period (see Table 1).

  2023 2024 2025
Revenue ($M) 53,662 71,252 91,955

Source: Gartner (May 2024)

AI Chips Revenue from Compute Electronics to Record Highest Share in Electronic Equipment Segment

In 2024, AI chips revenue from compute electronics is projected to total $33.4 billion, which will account for 47% of total AI semiconductors revenue. AI chips revenue from automotive electronics is expected to reach $7.1 billion, and $1.8 billion from consumer electronics in 2024.

Fierce Battle Between Semiconductor Vendors and Tech Companies

While much of the focus is on the use of graphics processing units (GPUs) for new AI workloads, the major hyperscalers (AWS, Google, Meta, and Microsoft) are all investing in developing their chips optimized for AI.

While chip development is expensive, 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,” Priestley added.



Subscribe to our Newsletter