Is Qualcomm’s On-Device AI a Game Changer for Mobile and PC?


1. On-device AI eliminates the need for constant internet connection, enabling usage in remote and offline scenarios.
2. Faster processing speeds enable real-time AI applications, leading to improved user experience.
3. Enhanced privacy and security: AI computations are performed locally, reducing data exposure to cloud servers.
4. Lower latency: On-device AI reduces latency by avoiding the need to transmit data to remote servers and waiting for a response.
5. Energy-efficient: Processing AI tasks locally consumes less power compared to transmitting data to the cloud.
6. Greater scalability: On-device AI allows for distributed processing across multiple devices, enabling more complex and resource-intensive applications.
7. Integration with existing hardware: Qualcomm’s experience with camera chipsets suggests seamless integration of AI capabilities into mobile and PC devices.


1. Limited computing power: On-device AI may have inherent limitations due to constraints on processing power compared to cloud-based solutions.
2. Restricted access to larger AI models: On-device AI may not have access to the same scale of pre-trained models available on cloud platforms.
3. Potentially limited updates and improvements: On-device AI may not receive frequent updates and improvements as readily as cloud-based solutions.
4. Limited data availability: On-device AI may have access to a smaller dataset compared to cloud servers, which could impact the accuracy and performance of AI applications.
5. Potential compatibility issues: On-device AI may face compatibility challenges with certain software or hardware configurations.
6. Higher device cost: Supporting on-device AI capabilities may increase the cost of mobile and PC devices.
7. Potential privacy concerns: Despite local processing reducing data exposure, there could still be privacy concerns if sensitive information is processed on-device.


At Snapdragon Summit 2023, Qualcomm unveiled its latest achievement in on-device artificial intelligence and machine learning systems. Building on years of experience with camera chipsets, the company showcased cutting-edge technologies.