Convert and run models on a target device, then measure accuracy, memory, and latency. Explore quantization and accelerator support while preserving a reproduci
Last reviewed: 2026-10-03
Edge AI runs inference near the source of observations, such as a phone, embedded device, or industrial gateway. Deployment work includes model conversion, runtime compatibility, hardware acceleration, and measurement on the actual target. LiteRT documents these stages for supported devices and model formats.
Deployment, inference, and optimization are related parts of this discipline. Establish a baseline before quantization or accelerator changes, then compare task quality, memory, startup time, and sustained latency. Device-local inference still needs a deliberate policy for stored observations, updates, and any network communication.
No. Operator support, runtime, hardware, and data transfer affect results. Measure the exact model and runtime on the intended device.