Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware
arXiv:2608.00008v2 Announce Type: replace Abstract: 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.
Operators using related systems should check whether Energy Efficiency of Locally Deployed LLMs changes compatibility, cost, or access requirements. For agent builders, this marks where planning, memory, or long-horizon behavior still breaks.