{ "version": "1", "updated": "2026-10-19", "data_version": "2026-10-19", "site": "https://leadgreen.ai", "name": "Lead Green Green Tokens API", "description": "GPU watt-hours per million output tokens by model, GPU and configuration, with grid carbon intensity and GPU hardware reference data. Static JSON, GET only, CORS open.", "endpoints": [ { "path": "manifest.json", "url": "https://leadgreen.ai/api/v1/manifest.json", "description": "Sources, versions, retrieval dates, licences and this endpoint list." }, { "path": "index.json", "url": "https://leadgreen.ai/api/v1/index.json", "description": "The index: 32 rows, one per model and GPU, energy-optimal under the latency limit, sorted ascending." }, { "path": "models.json", "url": "https://leadgreen.ai/api/v1/models.json", "description": "Every model on the index with its slug, Hugging Face id, parameter counts and measured GPUs." }, { "path": "models/{slug}.json", "url": "https://leadgreen.ai/api/v1/models/{slug}.json", "description": "One model: every measured configuration (concurrency, parallelism, latency, power) and the best row per GPU." }, { "path": "grid.json", "url": "https://leadgreen.ai/api/v1/grid.json", "description": "Grid emissions intensity, gCO2e per kWh, 2024 annual average, 193 countries plus World." }, { "path": "hardware.json", "url": "https://leadgreen.ai/api/v1/hardware.json", "description": "Manufacturer power ceilings, memory, MIG slice profiles and one embodied-carbon figure." }, { "path": "per-query.json", "url": "https://leadgreen.ai/api/v1/per-query.json", "description": "AI Energy Score text-generation rows, December 2025 round: Wh per 1,000 queries at batch size 1 on H100 80GB GPUs (one card for classes A and B, several for C)." }, { "path": "openapi.json", "url": "https://leadgreen.ai/api/v1/openapi.json", "description": "OpenAPI 3.1 description of these endpoints." }, { "path": "measurements.json", "url": "https://leadgreen.ai/api/v1/measurements.json", "description": "Contributed measurements accepted under the protocol, with contributor, affiliation, date, versions and MIG profile; never merged into index.json." }, { "path": "schema/measurement.schema.json", "url": "https://leadgreen.ai/api/v1/schema/measurement.schema.json", "description": "JSON Schema for a contributed measurement file (protocol v0.2); the build validates every submission against it." }, { "path": "changes.json", "url": "https://leadgreen.ai/api/v1/changes.json", "description": "Every change to the published data or its description: date, summary, documents, reason and source version." }, { "path": "index.csv", "url": "https://leadgreen.ai/api/v1/index.csv", "description": "The index rows as CSV, for a spreadsheet." }, { "path": "types.d.ts", "url": "https://leadgreen.ai/api/v1/types.d.ts", "description": "TypeScript types for every document, generated from the documents." }, { "path": "all.zip", "url": "https://leadgreen.ai/api/v1/all.zip", "description": "Every JSON document of the current release in one zip (the same bytes as the dated release zip)." }, { "path": "releases/index.json", "url": "https://leadgreen.ai/api/v1/releases/index.json", "description": "Every dated release of api/v1, each kept unchanged at releases// with a zip of the whole release, for citation." } ], "sources": [ { "name": "ML.ENERGY Leaderboard", "publisher": "The ML.ENERGY Initiative (started at SymbioticLab, University of Michigan)", "url": "https://ml.energy/leaderboard/", "data_url": "https://raw.githubusercontent.com/ml-energy/leaderboard/master/public/data/tasks/lm-arena-chat.json", "version": "3.0 (released 2025-12-01); data compiled 2026-02-16", "released": "2025-12-01", "compiled": "2026-02-16", "compiled_evidence": "the leaderboard's public/data/index.json last_updated, and the last commit to the task files, both 2026-02-16", "retrieved": "2026-09-26", "licence": "Apache-2.0 per the ml-energy/benchmark-v3 dataset card", "licence_evidence": [ "Hugging Face dataset ml-energy/benchmark-v3: tagged license:apache-2.0, ships a LICENSE file, and its dataset card's License section reads Apache-2.0", "The ML.ENERGY Leaderboard web app's footer says Apache 2.0", "The leaderboard repository (github.com/ml-energy/leaderboard) carries no licence file; its JSON files are reproduced here as published measurements until the ML.ENERGY team confirms that the dataset licence covers them" ], "workload": "Text Conversation (lm-arena-chat)", "method": "Energy per output token is steady-state GPU energy divided by the output tokens generated in the steady-state window (the benchmark's steady_state_energy_per_token), measured with Zeus under vLLM 0.11.1 on H100 80GB HBM3 and B200 servers. Energy per request is that figure multiplied by the average output length, not a separate measurement. Prompt processing is included.", "selection": "Each index row is the lowest-energy configuration for its model and GPU whose median inter-token latency does not exceed 500 ms, the leaderboard's own default ceiling; no measured configuration reaches it.", "cite": "Chung et al., The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization, NeurIPS 2025 Datasets and Benchmarks, arXiv 2505.06371", "cite_url": "https://arxiv.org/abs/2505.06371", "used_for": "index, per-model configurations, concurrency curves, measured GPU power", "configurations": "328 stable configurations: the leaderboard publishes the runs that passed its stability filter; every run, including the unstable ones, is in the gated Hugging Face dataset ml-energy/benchmark-v3" }, { "name": "ML.ENERGY Leaderboard", "publisher": "The ML.ENERGY Initiative (started at SymbioticLab, University of Michigan)", "url": "https://ml.energy/leaderboard/", "data_url": "https://raw.githubusercontent.com/ml-energy/leaderboard/master/public/data/tasks/gpqa.json", "version": "3.0 (released 2025-12-01); data compiled 2026-02-16", "released": "2025-12-01", "compiled": "2026-02-16", "compiled_evidence": "the leaderboard's public/data/index.json last_updated, and the last commit to the task files, both 2026-02-16", "retrieved": "2026-09-26", "licence": "Apache-2.0 per the ml-energy/benchmark-v3 dataset card", "licence_evidence": [ "Hugging Face dataset ml-energy/benchmark-v3: tagged license:apache-2.0, ships a LICENSE file, and its dataset card's License section reads Apache-2.0", "The ML.ENERGY Leaderboard web app's footer says Apache 2.0", "The leaderboard repository (github.com/ml-energy/leaderboard) carries no licence file; its JSON files are reproduced here as published measurements until the ML.ENERGY team confirms that the dataset licence covers them" ], "workload": "Problem Solving (gpqa)", "method": "Energy per output token is steady-state GPU energy divided by the output tokens generated in the steady-state window (the benchmark's steady_state_energy_per_token), measured with Zeus under vLLM 0.11.1 on H100 80GB HBM3 and B200 servers. Energy per request is that figure multiplied by the average output length, not a separate measurement. Prompt processing is included.", "selection": "Each index row is the lowest-energy configuration for its model and GPU whose median inter-token latency does not exceed 500 ms, the leaderboard's own default ceiling; no measured configuration reaches it.", "cite": "Chung et al., The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization, NeurIPS 2025 Datasets and Benchmarks, arXiv 2505.06371", "cite_url": "https://arxiv.org/abs/2505.06371", "used_for": "problem-solving versus chat comparison", "configurations": "187 stable configurations: the leaderboard publishes the runs that passed its stability filter; every run, including the unstable ones, is in the gated Hugging Face dataset ml-energy/benchmark-v3" }, { "name": "Ember, Yearly electricity data", "publisher": "Ember", "url": "https://ember-energy.org/data/yearly-electricity-data/", "data_url": "https://files.ember-energy.org/public-downloads/generation/outputs/release_generation_yearly_global.csv", "version": "October 2026 release (file last modified 2026-10-06)", "retrieved": "2026-10-17", "licence": "CC-BY-4.0", "factor_basis": "Lifecycle emissions intensity: CO2e per kWh of net generation, built from per-technology lifecycle factors (IPCC AR5 WG3 Annex III midpoints, all gases at GWP-100), so upstream methane, supply chains and plant manufacture are included. Production-based: imports and grid losses are excluded. A location-based figure for comparing places, not a Scope 2 combustion factor to file as reported.", "used_for": "grid emissions intensity of total generation, gCO2e per kWh, 2024 (location-based)" }, { "name": "NVIDIA Multi-Instance GPU User Guide, Supported MIG Profiles", "url": "https://docs.nvidia.com/datacenter/tesla/mig-user-guide/supported-mig-profiles.html", "version": "last updated 2026-09-11", "retrieved": "2026-09-26", "used_for": "mig_profiles", "licence": "manufacturer specifications transcribed as facts; NVIDIA retains its rights in the source documents" }, { "name": "NVIDIA product specifications: A100, H100, H200, DGX B200", "url": "https://www.nvidia.com/en-us/data-center/h100/", "version": "as published on the retrieval date", "retrieved": "2026-09-26", "used_for": "tdp (A100, H100, H200), systems", "licence": "manufacturer specifications transcribed as facts; NVIDIA retains its rights in the source documents" }, { "name": "NVIDIA, Product Carbon Footprint Summary for NVIDIA HGX B200", "url": "https://images.nvidia.com/aem-dam/Solutions/documents/HGX-B200-PCF-Summary.pdf", "version": "July 2025", "retrieved": "2026-09-26", "used_for": "tdp (B200), embodied", "licence": "manufacturer specifications transcribed as facts; NVIDIA retains its rights in the source documents" }, { "name": "AI Energy Score leaderboard", "publisher": "AI Energy Score (Hugging Face and Salesforce, with Neuralwatt, Cohere, Meta and Carnegie Mellon University)", "url": "https://huggingface.co/spaces/AIEnergyScore/Leaderboard", "data_url": "https://huggingface.co/spaces/AIEnergyScore/Leaderboard/raw/main/data/energy/text_generation.csv", "documentation": "https://huggingface.github.io/AIEnergyScore/", "version": "December 2025 round", "harness": "Neuralwatt's AI Energy Benchmarks for the December 2025 round; the February 2025 round ran on Optimum Benchmark", "retrieved": "2026-09-26", "licence": "Apache-2.0 as tagged on the Space; the project states no separate licence for the results file, which is reproduced as facts with attribution", "used_for": "GPU energy per 1,000 text-generation queries at batch size 1 on NVIDIA H100 80GB GPUs (one card for class A and B models, several for class C)" }, { "name": "OpenAI, What are tokens and how to count them", "publisher": "OpenAI", "url": "https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them", "version": "as published on the retrieval date", "retrieved": "2026-09-26", "licence": "cited, not redistributed", "used_for": "the rough words-per-token conversion" } ], "licence": "Compilation and site text: CC BY 4.0. Code: Apache 2.0. Third-party figures remain subject to their sources' terms, listed per source.", "caveat": "GPU energy only, measured at steady state on a busy server (the most energy-efficient configuration under a latency limit). Host, network, cooling and idle time are not counted. Every figure is a floor; a lightly used server sits well above it.", "boundary": "gpu_only", "publisher": { "name": "Lead Green", "parent": "Katonic", "url": "https://leadgreen.ai", "entity": "Katonic AI Technology Ltd", "registration": "CL9944", "jurisdiction": "Dubai International Financial Centre (DIFC), United Arab Emirates", "address": "Unit IH-00-VZ-01-FL-194, Level 1, Innovation Hub, Dubai International Financial Centre, Dubai, United Arab Emirates", "contact": "hello@leadgreen.ai", "response_working_days": 10 }, "attribution": "Green Tokens index, Lead Green, a Katonic project, https://leadgreen.ai/api/v1/, CC BY 4.0. Contains data from the ML.ENERGY Leaderboard v3.0 (The ML.ENERGY Initiative; data compiled 2026-02-16, retrieved 2026-09-26) and Ember Yearly Electricity Data (CC BY 4.0). Changes: [none | describe].", "releases": { "index": "https://leadgreen.ai/api/v1/releases/index.json", "pattern": "https://leadgreen.ai/api/v1/releases//" } }