Remote Inference
Remote inference refers to the execution of inference tasks on external servers rather than on local devices or machines. In this architecture, computational requests are sent to remote systems where models process inputs and return results to the client. This approach contrasts with local inference, where models run directly on the user’s device or edge hardware. Remote inference is particularly common in cloud-based AI services and server-based deployments of large language models.
Architecture and Workflow
In a remote inference setup, a client application sends input data to a remote server or service endpoint over a network connection. The server hosts the machine learning model and performs the actual computation, then returns the output back to the client. This separation of inference computation from the client application allows models to be centralized and shared across multiple users or applications simultaneously.
Common Use Cases and Advantages
Remote inference is widely used for serving large language models and other computationally intensive models where deploying models locally would be impractical due to hardware requirements or model size. It enables organizations to manage model updates and versioning centrally without requiring changes on client devices. Remote inference also allows for resource pooling and load balancing across multiple inference servers to handle varying demand efficiently.
Tradeoffs
The primary tradeoff of remote inference is latency—network communication between client and server introduces delays compared to local inference. Remote inference also creates dependencies on network availability and introduces privacy considerations since data must be transmitted to external servers. These factors make remote inference less suitable for real-time applications with strict latency requirements or scenarios where data cannot leave a user’s device.