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 large language models, where a centralized infrastructure handles model computation.
Advantages and Trade-offs
Remote inference offers several practical benefits. It eliminates the need for users to maintain or deploy models locally, reducing hardware requirements and reducing the burden of model updates. This allows organizations to scale inference across many concurrent requests by distributing load across server infrastructure. However, remote inference introduces latency due to network communication and creates dependency on external services. It also raises considerations around data privacy, since inputs and outputs transit through external systems.
Common Applications
Remote inference is standard practice for most commercial large language model APIs and cloud-based AI platforms. Organizations use it to serve models at scale without maintaining expensive local infrastructure, and to provide inference capabilities to users without requiring them to possess powerful local hardware. It also enables centralized monitoring, versioning, and fine-tuning of models across distributed clients.