Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers
Large language models (LLMs) are becoming increasingly capable at small parameter scales. At the same time, conventional cloud-centric deployment introduces challenges around data privacy, latency, and cost that are acute in operational technology and defence environments. Advances in model distillation, quantisation, and affordable edge accelerators now make local LLM inference on single-board computers feasible, but the high dimensionality of the configuration space makes identifying optimal deployments difficult without structured evaluation. Existing LLM-specific edge benchmarking efforts rely on CPU-only inference, poor coverage of genuine single-board computers, and generic evaluati...
| Authors | Harri Renney, Fouad Trad, Michael Mattarock et al. |
|---|---|
| Primary category | cs.AR |
| Published | 24 April 2026 |
| Last updated at source | 3 August 2026 |
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