Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware
The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, the energy costs on consumer hardware remain poorly characterized, as most benchmarks focus solely on accuracy. This paper presents a reproducible, hardware-level energy benchmark of 18 open-source LLMs (0.5B to 7B parameters) executed on a single consumer GPU (RTX 4060ti 16GB). Using the Ollama inference engine, GPU power draw was sampled at 2hz via nvidia-smi across a fixed prompt set. We evaluate mean/peak power, total energy per prompt (J/prompt), energy per output token (J/tok), and throughput (tok/s). Our findings suggest that factors bey...
| Authors | Philipp M. Zähl, Elja Dalipaj, Anika Hennig et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 June 2026 |
| Last updated at source | 25 September 2026 |
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