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Worldwide end-user spending on generative AI (GenAI) smartphones is projected to reach USD 298.2 billion by the end of 2025, according to Gartner, Inc, a business and technology research firm. This figure is expected to represent 20% of total AI end-user spending in 2025.
Gartner defines GenAI smartphones as devices equipped with a built-in neural engine or neural processing unit (NPU) capable of running small language models. The forecast includes both premium smartphones and basic smartphones (priced under USD 350), but excludes utility smartphones, which are not expected to support NPU capabilities at any time.
“Currently, most users still rely on text or touch inputs for tasks, and voice interactions remain limited in scope. However, as conversational AI becomes more natural, users are likely to become more comfortable with AI as a proactive digital assistant rather than merely a reactive tool,” said Ranjit Atwal, Senior Director Analyst at Gartner.
With more mobile manufacturers incorporating on-device GenAI models and applications, end-user spending on GenAI smartphones is expected to rise to USD 393.3 billion in 2026 – a 32% increase from 2025 (see Table 1). Gartner anticipates that all premium smartphones will include GenAI capabilities by 2029.
Worldwide GenAI smartphone end-user units and spending, 2024–2026
| Year | GenAI Smartphone Units (thousands) | End-User Spending (USD millions) |
|---|---|---|
| 2024 | 260,433.4 | 244,735.1 |
| 2025 | 369,347.0 | 298,189.5 |
| 2026 | 559,000.6 | 393,297.7 |
Source: Gartner (September 2025)
“The widespread adoption of NPUs in smartphones will enable GenAI models to run more quickly and efficiently. This will likely encourage users to upgrade to newer hardware in order to benefit from improved performance,” Atwal added. “In 2025, Gartner forecasts that nearly all premium GenAI smartphones will include NPUs, while 41% of basic GenAI smartphones will be similarly equipped.”
By 2027, Gartner expects the performance of on-device NPUs in premium GenAI smartphones to exceed 40 tera operations per second (TOPS), enabling real-time processing of complex multimodal AI workloads without excessive power consumption.