Client Hardware

Balancing AI inference between server and edge for smarter cost control

Splitting inference across server and client hardware is a practical answer to AI's rising cost problem. The idea of keeping proprietary weights server-side while offloading part of the computation to user devices is worth serious attention. The real challenge is coordination: training two models to communicate through latent tensors is promising, though the complexity is substantial. If the protocol becomes standardized, the flexibility could extend beyond one-to-one setups. For deeper context on distributed systems, our guide on distributed algorithms explores similar terrain.