Energy efficiency: Running inference on specialized hardware at the edge can be more energy-efficient than sending all data to centralized data centers for processing.
Fault tolerance: Distributed inference can improve system reliability by allowing continued operation even if some nodes fail.
Privacy and security: Keeping sensitive data local for inference can enhance data privacy and security compared to sending all data to a central location.
Scalability: As AI models grow more complex, distributing inference across multiple nodes allows for better scaling of computational resources.
The integration of inference computing into distributed infrastructure is driving innovations in areas like autonomous vehicles, smart cities, industrial IoT, and personalized mobile applications. It's also spurring the development of specialized hardware and software optimized for distributed inference tasks.