Energy you can trace, not just estimate#
Carbon reporting for AI is usually a blended estimate. LumenFlow goes finer: where the signals exist, it attributes energy consumption (in joules) per compute run, and even across the phases of a model call, then derives carbon from that.
Monitoring power and electricity#
For a governed compute campus, LumenFlow ingests the facility's power and carbon-intensity signals and its PUE (power usage effectiveness), and attributes the power draw and energy each compute run uses — in joules, down to the individual token and the prefill/decode phases of a model call. The result is an auditable picture of electricity use per tenant, per workload, and per run, instead of a single monthly figure.
The boundary holds: LumenFlow monitors, attributes, and proves power and energy — it does not operate the electricity, power, or cooling itself (see Governed compute campuses).
Provenance is part of the number#
Every energy and carbon figure carries a provenance tier:
- Measured — derived from real signals from the compute.
- Modelled — computed from a defensible model where direct measurement isn't available.
- Estimated — a coarser fallback.
Reporting the tier alongside the figure is the point. An auditor or sustainability lead can see which numbers are grounded in measurement and which are approximations, instead of being handed one opaque total.
Where it comes from#
Energy provenance is strongest for compute LumenFlow has signal into — dedicated campus compute and connected runners — and rides the same evidence chain as the rest of your records, so the figures are part of the verifiable trail. See Governed compute campuses.
info LumenFlow does not claim a precision it doesn't have. The honest tiering is the feature: a measured figure and an estimated figure are never presented as the same thing.