AI Power Consumption: Local vs Cloud and the Scale Problem
Local AI on Apple Silicon uses 50-200W with negligible environmental impact. Cloud AI's energy problem is scale (data centers, cooling, 24/7 operation), not the technology itself.
Running AI locally on a modern device like an Apple M-series Mac uses 50-200 watts — equivalent to a few light bulbs. The M1/M2/M3 chips have a dedicated Neural Engine and shared memory architecture that makes local AI inference remarkably efficient, with no separate GPU required. Cloud AI services consume vastly more energy because of: - Data center cooling (often using water-based systems) - Network infrastructure for transmitting data back and forth - Redundant servers running 24/7 regardless of demand - The overhead of serving millions of concurrent users If everyone ran AI locally, the total energy impact would likely be lower (not just the same impact spread out) because idle local devices consume near-zero power, while data centers maintain constant baseline consumption. However, local devices cannot match the capability of large cloud models that require hundreds of GPUs. The environmental criticism of AI is primarily about the scale of cloud inference and training, not the technology itself. A single user running a 7B parameter model locally has negligible environmental impact.