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GreenBench: Benchmarking Energy Efficiency and Carbon Footprint of Open-Source LLM Inference on Apple Silicon
AI InsightGreenBench provides the first energy-efficiency benchmark for LLM inference on Apple Silicon's unified memory architecture, measuring only 0.47 W CPU+GPU package power on M4 Pro. Unlike prior Green AI work focused on datacenter GPUs, this extends measurement to Apple Silicon, filling a data gap in on-device inference carbon footprints.Key TakeawayLLM inference energy measurement extends to Apple Silicon unified memory for the first time.Why It MattersProvides quantifiable environmental costs for on-device inference, influencing Green AI research directions and low-power deployment choices.Who's Affected- AI ResearchersGain an energy baseline for Apple Silicon to compare carbon footprints across hardware architectures.
- DevelopersPower reference for deploying local LLMs on Apple Silicon, enabling low-power design choices.
- AppleThird-party validation of chip energy efficiency strengthens on-device AI competitiveness.
- Green Computing PractitionersExtends Green AI assessment from datacenters to edge devices.
What's NextWatch whether GreenBench expands to more chips, model scales, and real-world workloads, and whether power numbers are reproducible.Importance 70/100