Build a Generative Workload Power and Carbon Ledger Reconciler

Correlate per-request batch tokens with measured accelerator power sensor logs. Synthesize carbon footprint summaries and efficiency improvement plans using hosted Claude.

Datacenter Sustainability Lead

Ingest normalized time-series power telemetry from GPU power sensors and batch serving token counts.

Compute joules-per-token and convert total watt-hours to carbon equivalents using local regional grid emission factors.

Invoke hosted Claude to identify power-inefficient batching patterns and draft optimization proposals.

Produce an energy ledger report detailing departmental carbon footprints and recommended power caps.

Static HTML sustainability report generator and downloadable CSV carbon allocation ledger.

Normalized power telemetry logs (Watts) paired with synthetic continuous batching request logs containing input/output token counts. Starter examples below are synthetic. They are not downloaded records.

NVIDIA DCGM exporter Utilizes DCGM exporter power consumption metrics (DCGM_FI_DEV_POWER_USAGE) to calculate physical energy draw.

evidently-ai, anthropic. Evidently AI monitors data drift and distribution changes in power-per-token consumption over time. Hosted a currently available hosted Claude model accessed via Anthropic Python SDK; purely analytical role.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Calculates exact watt-hours and joules-per-token metrics from telemetry.

Applies regional emission factors accurately to produce kgCO2e values.

Synthesizes actionable efficiency advice via hosted Claude without fabricated metrics.

```json [ { "input": { "team": "SearchPlatform", "tokens_processed": 5000000, "duration_hours": 10, "avg_power_watts": 350, "grid_carbon_intensity_g_kwh": 380 }, "expected_output": { "total_kwh": 3.5, "joules_per_token": 2.52, "total_emissions_kg_co2": 1.33, "sustainability_advice": "Joules per token are within standard bounds. Recommend dynamic power capping during low-traffic windows." } }, { "input": { "team": "DocumentSummarizer", "tokens_processed": 50000, "duration_hours": 8, "avg_power_watts": 320, "grid_carbon_intensity_g_kwh": 400 }, "expected_output": { "total_kwh": 2.56, "joules_per_token": 184.32, "total_emissions_kg_co2": 1.02, "sustainability_advice": "Extremely high joules per token detected due to low batch occupancy. Recommend request queue pooling to increase continuous batch utilization." } } ] ```

Calculations rely solely on explicitly provided telemetry; does not enforce hardware power caps directly. Use mock external writes. Require human review before any real deployment.