AI Chipset Shipments Reach 1.3 Billion in 2030, Bringing On-Device AI to Smartphones, PCs, Tablets, and Gaming Consoles

Heterogenous AI chipsets capable of distributing workloads between CPU, GPU, and NPU are highly effective architectures for tackling today's diverse AI workloads – including generative and multimodal AI – and these systems will be adopted across device markets at an accelerating rate. More demanding on-device AI workloads in PCs will be addressed by GPU cards, like NVIDIA's RTX, and AMD's high-end Radeon hardware. This is why these chipsets, which reside outside of heterogenous systems, will remain in the AI mix going forward.

For on-device AI in personal and work devices to fulfill its potential, application development incentives like Intel's AI PC Acceleration Program and the in-house work from captive vendors Apple and Google will need to bear fruit to convince consumer and enterprise customers to pay a premium. "ABI Research believes productivity AI applications can reduce refresh cycles of smartphones, notebooks, and desktops – as well as other AI applications in tablets and gaming consoles – and provide the incentive to bring about this uptick in demand," Schell concludes.

These findings are from ABI Research's Artificial Intelligence and Machine Learning: Personal & Work Devices market data report. This report is part of the company's AI & Machine Learning research service, which includes research, data, and ABI Insights.

About ABI Research

ABI Research is a global technology intelligence firm uniquely positioned at the intersection of technology solution providers and end-market companies. We serve as the bridge that seamlessly connects these two segments by providing exclusive research and expert guidance to drive successful technology implementations and deliver strategies proven to attract and retain customers.

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