Source: Semianalysis
The inference location question—whether AI models process data locally on devices or remotely in datacenters—determines latency, privacy, cost, and who controls the user experience. On-device inference reduces dependency on internet connectivity and server infrastructure, but requires smaller models and expensive chip integration; datacenter inference offers computational flexibility and model sophistication, but creates data surveillance risks and network bottlenecks that make real-time applications like autonomous systems or AR unreliable. Companies are making stack decisions that will fragment the AI market into specialized ecosystems rather than consolidate around a single deployment model.