On-device inference

Execute machine learning models directly on edge hardware (smartphones, IoT) to achieve sub-100ms latency and maximize data privacy.

On-device inference shifts the AI workload from the cloud to the edge: processing occurs locally on the user’s device, not a remote server. This architecture is critical for use cases demanding real-time response, like mobile vision and voice assistants, by eliminating network latency. Key industry players like Qualcomm and Google drive this with specialized hardware (NPUs) and optimized models, often using quantization to compress models for resource-constrained devices. The primary benefits are immediate: enhanced privacy (data remains local), guaranteed functionality (no internet dependency), and reduced cloud compute costs for developers.

https://www.ibm.com/topics/ai-inference