Local vs cloud: the energy trade-off
Running a model on your own machine isn't automatically greener — here's how to think about it.
Updated 31 May 2026
| Task | Local model | Cloud model |
|---|---|---|
| Per-answer efficiency | Often lower | Often higher |
| Uses purpose-built hardware | Rarely | Yes |
| Idle / overhead waste | Your device | Shared at scale |
| Greenest when… | Your grid is clean | Provider grid is clean |
| Data privacy | Stays local | Leaves your control |
It’s tempting to assume that running a model locally is the greener choice because “nothing leaves your machine”. The energy picture is more nuanced — and sometimes points the other way.
Why cloud can be more efficient per answer
Large providers run purpose-built accelerators at high utilisation, in data centres tuned for efficiency, often on increasingly clean energy. A consumer GPU doing the same work is usually less efficient per answer, and a powerful machine left running draws power whether or not you’re using it.
Where local wins
Local avoids the network and shared-infrastructure overhead, and a small model on modest hardware can be genuinely light. Crucially, the deciding factor is often your local electricity grid versus the provider’s — clean power changes the answer.
How to read the table
These are tendencies, not guarantees; the real footprint depends on the model size, your hardware, utilisation, and grid. Local’s clearest, most reliable advantage isn’t energy at all — it’s privacy.
Don’t pick local for the planet by default. Pick it for privacy and control; choose the greener option case by case, with your grid in mind.