Cloud AI and edge AI: where the decision happens
Cloud AI runs processing in remote infrastructure. Edge AI performs inference near the sensor or machine. A hybrid design can keep a time-sensitive decision local while sending selected data for fleet analysis and model development. The important question is which function must remain available when the network is slow or unavailable.
Consider a visual inspection station. The camera acquires a part image, software prepares it, the model evaluates it and an interface reports the result. Uploading each image introduces a network dependency into that path. Local inference changes that dependency, but the device still needs enough processing capacity and a way to manage software versions.
Compare the complete workload
Measure acquisition, preprocessing, inference and output handling together. An AI benchmark that times only the model can miss the bottleneck. Record sustained latency, memory use, power and behavior after warm-up. Run the same test with realistic image sizes, simultaneous interfaces and the intended enclosure.
Deep learning, generative AI and device resources
Deep learning models differ in memory footprint and supported operations. Generative AI can introduce further memory and response-time demands. Choose a model and runtime that meet the actual task before specifying an AI computer. A processor advertised for AI is not a substitute for testing the deployed model.
Define offline behavior and data ownership
Decide which records stay on the device, which leave it and how long each is retained. Local processing can reduce transmitted raw data, but secure updates, access control and diagnostics remain necessary. Define what the operator sees when inference is unavailable and how the system returns to normal operation.
Build a measurable architecture brief
Bring a representative input sample, the required decision time, interface list, power limit and installation conditions. Alvexis can use those constraints to scope custom electronics, embedded software and enclosure integration. The first milestone should be a working measurement of the proposed processing path, with clear criteria for continuing or changing the architecture.