Fog computing architectures distribute compute, storage, and networking services between cloud data centers and edge devices. Adding LLMs to this stack introduces a clear tension: edge hardware is too constrained to run large models, yet shipping raw telemetry to the cloud for inference wastes bandwidth and exposes unpredictable costs. The pragmatic path is to treat fog nodes as intelligent aggregation gateways that preprocess data, maintain local state, and call cloud inference APIs only when reasoning complexity demands it. This article explores concrete patterns for wiring LLMs into fog layers, with a focus on cost control, latency management, and SDK compatibility.
Architecture Patterns for LLM Inference in Fog
A typical three-tier hierarchy places sensors and actuators at the edge, fog nodes in local gateways or micro data centers, and the cloud as the model authority. The fog layer handles protocol translation, data normalization, and request batching. When an edge device generates a natural language query or a large telemetry dump, the fog node decides whether to resolve it
