wattindex

wattindex

How much GPU energy does it take to serve a million output tokens? wattindex answers from a public index of measured configurations: watt-hours per million output tokens by model, GPU and running batch, with grid carbon intensity to price the energy anywhere.

• Best-row selection keeps only rows whose median inter-token latency is at or under your deadline (itl_deadline_ms, default 500 ms) and the lowest energy wins.
• estimate scales a row to any token count, adds kg CO2e for a country's grid, and — when you supply pue and/or system_power_ratio (each at least 1.0) — energy at the meter, labelled as your assumptions.
• Every response states its boundary (gpu_only), the caveat from the index, and whether the data came from the live API, a bundled snapshot, or a local folder, with update date and data version.

Data is read from the JSON API base in LEADGREEN_API_BASE (default https://leadgreen.ai/api/v1); a non-URL value is a local folder holding the API files, for fully-offline use. All lookups and arithmetic happen locally.

CLI

npx @reneturcios/wattindex list-models
npx @reneturcios/wattindex energy --model llama-3.1-70b --gpu H100
npx @reneturcios/wattindex estimate --model llama-3.1-70b --output-tokens 5000000 --country DE --pue 1.2
npx @reneturcios/wattindex compare-gpus --model llama-3.1-70b
npx @reneturcios/wattindex concurrency-curve --model llama-3.1-70b --gpu H100
npx @reneturcios/wattindex grid-intensity --country World
npx @reneturcios/wattindex gpu-hardware --gpu B200
npx @reneturcios/wattindex sources
npx @reneturcios/wattindex --version

Output is JSON. Exit codes: 0 on success, 1 on a runtime error (unknown model/GPU/country, unreadable or unreachable dataset), 2 on a usage error.

MCP server

npx -y @reneturcios/wattindex mcp

MCP client config:

{
  "mcpServers": {
    "wattindex": {
      "command": "npx",
      "args": ["-y", "@reneturcios/wattindex", "mcp"]
    }
  }
}

Tools

• list_models — models on the index and the GPUs each has measurements for
• energy_per_million_tokens — best row per GPU for a model
  • model (string, required), gpu (string, optional), itl_deadline_ms (number, optional, default 500)
• estimate — GPU energy for a token count, kg CO2e, optional energy at the meter
  • model (string, required), output_tokens (number, required), gpu, country, running_batch, num_gpus, itl_deadline_ms, pue, system_power_ratio (optional)
• compare_gpus — H100 versus B200 best rows for a model
  • model (string, required), itl_deadline_ms (optional)
• concurrency_curve — energy at every measured running batch
  • model, gpu (strings, required)
• grid_intensity — annual average gCO2e per kWh for a country (name, short name, ISO alpha-2/alpha-3, or World)
  • country (string, required)
• gpu_hardware — max TDP, memory, MIG profiles, whole-system ceilings
  • gpu (string, optional; omit to list every GPU)
• sources — every source, version, retrieval date and licence

Docker

docker run -i ghcr.io/cloudcorprecords/wattindex
docker run --rm ghcr.io/cloudcorprecords/wattindex grid-intensity --country DE

Development

npm install
npm run build   # tsc -> dist/
npm test        # tsx --test test/*.test.ts (fake loader, no network)
npm run dev     # run the CLI from source

License

MIT — Copyright (c) 2026 Rene Turcios