Semiconductors designed to execute artificial intelligence (AI) workloads will represent a $53.4 billion revenue opportunity for the semiconductor industry in 2023, an increase of 20.9% from 2022, according to the latest forecast from Gartner, Inc.
“The developments in generative AI and the increasing use of a wide range AI-based applications in data centres, edge infrastructure and endpoint devices require the deployment of high performance graphics processing units (GPUs) and optimised semiconductor devices. This is driving the production and deployment of AI chips,” said Alan Priestley, VP Analyst at Gartner.
AI semiconductor revenue will continue to experience double-digit growth through the forecast period, increasing 25.6% in 2024 to $67.1 billion. By 2027, AI chips revenue is expected to be more than double the size of the market in 2023, reaching $119.4 billion.
Many more industries and IT organisations will deploy systems that include AI chips as the use of AI-based workloads in the enterprise matures. In the consumer electronics market, Gartner analysts estimate that by the end of 2023, the value of AI-enabled application processors used in devices will amount to $1.2 billion, up from $558 million in 2022.
The need for efficient and optimised designs to support cost effective execution of AI-based workloads will result in an increase in deployments of custom-designed AI chips. “For many organisations, large scale deployments of custom AI chips will replace the current predominant chip architecture – discrete GPUs – for a wide range of AI-based workloads, especially those based on generative AI techniques,” said Priestley.
Generative AI is also driving demand for high-performance computing systems for development and deployment, with many vendors offering high performance GPU-based systems and networking equipment seeing significant near-term benefits. In the long term, as the hyper-scalers look for efficient and cost-effective ways to deploy these applications, Gartner expects an increase in their use of custom-designed AI chips.
