Edge-First Language Model Inference: Models, Metrics, and Tradeoffs
It addresses the problem of efficient and adaptive language model inference for industries seeking to reduce costs, latency, and improve reliability and privacy across heterogeneous computing environments, but it is incremental as it builds on existing model compression and benchmarking approaches.
This work examines the deployment of Small Language Models (SLMs) on edge devices versus cloud systems, identifying scenarios where edge inference provides comparable performance with lower costs and others requiring cloud fallback due to scalability or capacity limits.
The widespread adoption of Language Models (LMs) across industries is driving interest in deploying these services across the computing continuum, from the cloud to the network edge. This shift aims to reduce costs, lower latency, and improve reliability and privacy. Small Language Models (SLMs), enabled by advances in model compression, are central to this shift, offering a path to on-device inference on resource-constrained edge platforms. This work examines the interplay between edge and cloud deployments, starting from detailed benchmarking of SLM capabilities on single edge devices, and extending to distributed edge clusters. We identify scenarios where edge inference offers comparable performance with lower costs, and others where cloud fallback becomes essential due to limits in scalability or model capacity. Rather than proposing a one-size-fits-all solution, we present platform-level comparisons and design insights for building efficient, adaptive LM inference systems across heterogeneous environments.