AI Companies Have Targeted Smartphone Hardware Development
Developers aim to run 100-billion-parameter AI models locally on mobile devices by 2028.
Updated on Oct. 12, 2026 in Artificial Intelligence
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AI companies have begun pursuing proprietary smartphone and wearable hardware to support advanced machine learning. Qualcomm has confirmed these firms are seeking the compute capacity to run 100-billion-parameter models continuously in the background.
Why it matters
This shift marks a move by AI developers to bypass current hardware limitations that restrict large models to cloud servers. By internalizing hardware design, these companies aim to enable sophisticated, persistent AI agents that operate directly on a user's device.
The current development goal targets support for 100-billion-parameter models, a significant scale jump compared to existing mobile-optimized models. Qualcomm is the primary chip designer engaged in discussions to architect hardware capable of sustaining these loads.
The players
Qualcomm
A leading semiconductor and telecommunications equipment company that designs system-on-chip architectures for mobile devices.
Cristiano Amon
The Chief Executive Officer of Qualcomm who oversees the company's strategy for mobile AI hardware integration.
OpenAI
A prominent artificial intelligence research and deployment company currently reported to be developing dedicated wearable and mobile hardware.
The details
Companies are seeking to run large language models—complex algorithms trained on vast datasets to generate human-like text—locally on mobile devices. This requires specialized silicon capable of managing high-bandwidth memory and thermal constraints during continuous operation. By moving these tasks from the cloud to the device, they aim to reduce latency and improve privacy for persistent, background-running AI services.
Timeline
2028 marks the target year for enabling 100-billion-parameter model operation on smartphones.
The Tech Race
This move signals a shift away from the traditional ecosystem reliance on established manufacturers like Samsung. It pits current software-first AI companies against incumbent hardware giants in a race to define the next mobile architecture standard.
Users should expect a potential shift toward specialized devices that promise continuous, low-latency AI interaction without cloud dependency. These capabilities remain in the development phase, with no consumer-ready hardware currently available on the market.
The takeaway
The industry is trending toward vertical integration, where the creators of large-scale models also design the hardware they run on. Watch for hardware architecture disclosures or prototype announcements from AI software firms ahead of the 2028 performance targets.
Further reading
Explore the current landscape of mobile machine learning in our Artificial Intelligence section.
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