Inference computing plays an increasingly important role in distributed infrastructure, particularly as AI and machine learning applications become more prevalent. Here's how inference computing fits into the distributed infrastructure landscape:
Edge AI: Inference computing is often performed at the edge of networks, closer to where data is generated. This reduces latency and bandwidth usage, which is crucial for real-time applications.
Load distribution: By running inference tasks on distributed nodes, the workload can be spread across multiple devices or servers, improving overall system performance and scalability.
IoT integration: Many IoT devices now incorporate inference capabilities, allowing for local processing of sensor data without constant communication with a central server.
Personalization: Distributed inference enables personalized services by processing user-specific data locally, enhancing privacy and reducing central server load.