Responsible computePerformance track
Energy & carbon
Read it in Chapter 5 · checkpoint +50 XP →
kWh, PUE and CO₂e: measuring the environmental cost of compute so it can be managed, not guessed.
A GPU cluster is a power plant's customer. A rough accounting:
facility energy (kWh) = IT energy (GPUs + hosts + network) × PUE emissions (kg CO₂e) = facility energy × grid carbon intensity
- PUE (Power Usage Effectiveness) captures cooling and power-delivery overhead: ~1.1 for the best liquid-cooled sites, 1.3–1.6 for typical ones.
- Carbon intensity varies by more than 10× between regions and by time of day. Where and when you train can matter as much as how.
Levers you control
- Efficiency first: higher MFU means fewer GPU-hours for the same model.
- Power caps trade a little speed for a lot of energy.
- Right-size: don't leave idle allocations running. Schedulers can reclaim them.
- Report kWh and CO₂e per run, next to cost and accuracy, so trade-offs are visible.
Every LazyLab run shows its energy, cost and carbon in the header.
Use it in a mission
Performance
Watts Up
The facility asks for a 20% power cut. Keep at least 85% of the throughput.
Architecture
Build the Pod
Spec a GPU pod that trains a 13B model in two weeks, within budget and the facility's power envelope.
Related
Sources & further reading
- Carbon Emissions and Large Neural Network Training (Patterson et al., 2021)
- Quantifying the Carbon Emissions of ML (Lacoste et al., 2019)
Written for LazyLab in our own words; sources are cited for depth and verification.