Datasets:

leadgreen
/

green-token-index

model stringlengths 9 38	slug stringlengths 9 38	model_id stringlengths 13 49	params_b float64 8 671	active_params_b float64 3 405	precision stringclasses 2 values	gpu stringclasses 2 values	num_gpus int64 1 8	max_num_seqs int64 128 4.1k	avg_running_batch float64 128 3.76k	wh_per_million_output_tokens float64 13 573	joules_per_output_token float64 0.05 2.06	median_itl_ms float64 33.9 272	gpu_power_watts_total int64 589 7.53k	avg_output_tokens int64 351 1.46k	row_id stringlengths 20 49
Llama 3.1 8B Instruct	llama-3-1-8b-instruct	meta-llama/Llama-3.1-8B-Instruct	8	8	BF16	B200	1	2,048	2,042.9	12.97	0.046696	124.9	691	396	llama-3-1-8b-instruct/B200/1/2048
Qwen 3 8B	qwen-3-8b	Qwen/Qwen3-8B	8	8	BF16	B200	1	1,536	1,426.7	14.74	0.053058	91.2	801	649	qwen-3-8b/B200/1/1536
Qwen 3 14B	qwen-3-14b	Qwen/Qwen3-14B	14	14	BF16	B200	1	2,048	2,044.6	15.21	0.054771	112.6	869	613	qwen-3-14b/B200/1/2048
Qwen 3 30B A3B Instruct	qwen-3-30b-a3b-instruct	Qwen/Qwen3-30B-A3B-Instruct-2507	30	3	BF16	B200	1	1,024	1,022.6	15.67	0.056408	88.7	601	928	qwen-3-30b-a3b-instruct/B200/1/1024
Llama 3.1 8B Instruct	llama-3-1-8b-instruct	meta-llama/Llama-3.1-8B-Instruct	8	8	BF16	H100	1	512	510.9	19.5	0.070195	47.4	688	397	llama-3-1-8b-instruct/H100/1/512
Qwen 3 8B	qwen-3-8b	Qwen/Qwen3-8B	8	8	BF16	H100	1	512	474.4	20.04	0.072129	47.2	616	645	qwen-3-8b/H100/1/512
Qwen 3 30B A3B Instruct	qwen-3-30b-a3b-instruct	Qwen/Qwen3-30B-A3B-Instruct-2507	30	3	BF16	H100	2	1,024	1,018.8	24.69	0.088877	74.8	1,123	920	qwen-3-30b-a3b-instruct/H100/2/1024
Gemma 3 12B	gemma-3-12b	google/gemma-3-12b-it	12	12	BF16	B200	1	512	486.6	26.49	0.095356	43.5	928	723	gemma-3-12b/B200/1/512
Qwen 3 14B	qwen-3-14b	Qwen/Qwen3-14B	14	14	BF16	H100	1	256	253.4	31.09	0.111917	38.4	645	625	qwen-3-14b/H100/1/256
Qwen 3 32B	qwen-3-32b	Qwen/Qwen3-32B	32	32	BF16	B200	1	384	383.3	39.05	0.140581	50.2	972	635	qwen-3-32b/B200/1/384
NVIDIA Nemotron Nano 12B V2	nvidia-nemotron-nano-12b-v2	nvidia/NVIDIA-Nemotron-Nano-12B-v2	12	12	BF16	B200	1	256	255.6	39.43	0.141962	33.9	885	1,464	nvidia-nemotron-nano-12b-v2/B200/1/256
NVIDIA Nemotron Nano 9B V2	nvidia-nemotron-nano-9b-v2	nvidia/NVIDIA-Nemotron-Nano-9B-v2	9	9	BF16	B200	1	256	255.3	40.94	0.147368	40.7	800	1,242	nvidia-nemotron-nano-9b-v2/B200/1/256
NVIDIA Nemotron Nano 9B V2	nvidia-nemotron-nano-9b-v2	nvidia/NVIDIA-Nemotron-Nano-9B-v2	9	9	BF16	H100	1	256	255.3	43.11	0.155207	43.4	693	510	nvidia-nemotron-nano-9b-v2/H100/1/256
Gemma 3 12B	gemma-3-12b	google/gemma-3-12b-it	12	12	BF16	H100	1	128	127.8	46.14	0.166113	34.7	589	723	gemma-3-12b/H100/1/128
Gemma 3 27B	gemma-3-27b	google/gemma-3-27b-it	27	27	BF16	B200	1	256	247.3	49.1	0.176746	39.6	974	717	gemma-3-27b/B200/1/256
NVIDIA Nemotron Nano 12B V2	nvidia-nemotron-nano-12b-v2	nvidia/NVIDIA-Nemotron-Nano-12B-v2	12	12	BF16	H100	1	256	241.2	50.08	0.180303	46	694	462	nvidia-nemotron-nano-12b-v2/H100/1/256
Llama 3.1 70B Instruct	llama-3-1-70b-instruct	meta-llama/Llama-3.1-70B-Instruct	70	70	BF16	B200	2	2,048	2,041.9	52.41	0.188675	140.5	1,907	351	llama-3-1-70b-instruct/B200/2/2048
Llama 3.3 70B Instruct	llama-3-3-70b-instruct	meta-llama/Llama-3.3-70B-Instruct	70	70	BF16	B200	2	1,024	1,021.6	60.72	0.218586	105.6	1,935	409	llama-3-3-70b-instruct/B200/2/1024
Qwen 3 32B	qwen-3-32b	Qwen/Qwen3-32B	32	32	BF16	H100	2	256	255.6	63.47	0.228481	44.5	1,178	634	qwen-3-32b/H100/2/256
Qwen 3 235B A22B Instruct	qwen-3-235b-a22b-instruct	Qwen/Qwen3-235B-A22B-Instruct-2507	235	22	BF16	B200	4	4,096	3,764.6	66.73	0.240245	67.8	3,196	874	qwen-3-235b-a22b-instruct/B200/4/4096
Gemma 3 27B	gemma-3-27b	google/gemma-3-27b-it	27	27	BF16	H100	2	192	187	80.42	0.28952	39.8	1,210	714	gemma-3-27b/H100/2/192
Llama 4 Scout 17B 16E Instruct	llama-4-scout-17b-16e-instruct	meta-llama/Llama-4-Scout-17B-16E-Instruct	109	17	BF16	H100	4	512	472.8	93.39	0.336198	69.1	1,881	402	llama-4-scout-17b-16e-instruct/H100/4/512
Llama 3.1 70B Instruct	llama-3-1-70b-instruct	meta-llama/Llama-3.1-70B-Instruct	70	70	BF16	H100	4	1,024	898.5	103.65	0.373142	105.6	2,548	351	llama-3-1-70b-instruct/H100/4/1024
Llama 3.3 70B Instruct	llama-3-3-70b-instruct	meta-llama/Llama-3.3-70B-Instruct	70	70	BF16	H100	4	768	724.2	117.53	0.423094	97.5	2,546	413	llama-3-3-70b-instruct/H100/4/768
Llama 4 Maverick 17B 128E Instruct FP8	llama-4-maverick-17b-128e-instruct-fp8	meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8	400	17	FP8	H100	8	384	383.3	134.5	0.484193	47.9	3,462	408	llama-4-maverick-17b-128e-instruct-fp8/H100/8/384
Qwen 3 235B A22B Instruct FP8	qwen-3-235b-a22b-instruct-fp8	Qwen/Qwen3-235B-A22B-Instruct-2507-FP8	235	22	FP8	B200	2	256	255.7	139.08	0.500694	64.5	1,891	876	qwen-3-235b-a22b-instruct-fp8/B200/2/256
DeepSeek V3.1	deepseek-v3-1	deepseek-ai/DeepSeek-V3.1	671	37	FP8	B200	8	3,072	3,007.5	168.43	0.606344	185.5	5,470	842	deepseek-v3-1/B200/8/3072
Qwen 3 235B A22B Instruct FP8	qwen-3-235b-a22b-instruct-fp8	Qwen/Qwen3-235B-A22B-Instruct-2507-FP8	235	22	FP8	H100	8	512	511.2	205.96	0.74147	75.8	3,961	881	qwen-3-235b-a22b-instruct-fp8/H100/8/512
Llama 3.1 405B Instruct	llama-3-1-405b-instruct	meta-llama/Llama-3.1-405B-Instruct	405	405	BF16	B200	8	4,096	3,588.7	281.96	1.015042	272.3	7,530	370	llama-3-1-405b-instruct/B200/8/4096
Qwen 3 235B A22B Instruct	qwen-3-235b-a22b-instruct	Qwen/Qwen3-235B-A22B-Instruct-2507	235	22	BF16	H100	8	256	228.5	303.53	1.09271	55.5	4,038	874	qwen-3-235b-a22b-instruct/H100/8/256
Llama 3.1 405B Instruct FP8	llama-3-1-405b-instruct-fp8	meta-llama/Llama-3.1-405B-Instruct-FP8	405	405	FP8	B200	4	512	510.5	318.53	1.146695	98.2	3,883	380	llama-3-1-405b-instruct-fp8/B200/4/512
Llama 3.1 405B Instruct FP8	llama-3-1-405b-instruct-fp8	meta-llama/Llama-3.1-405B-Instruct-FP8	405	405	FP8	H100	8	256	255.2	572.94	2.062586	66.9	4,875	367	llama-3-1-405b-instruct-fp8/H100/8/256

Green Tokens: GPU energy per million output tokens

Lead Green is a public index of the GPU energy it takes to serve a million output tokens (watt-hours per million output tokens), by model, GPU and configuration, with grid carbon intensity to price it anywhere, published as open data with a JSON API and an MCP server under CC BY 4.0. Every figure comes from a named public source with a retrieval date; it is a Katonic project, run and paid for by Katonic, and takes no money from hardware, cloud or model vendors.

This dataset is the 2026-10-19 release of the Lead Green JSON API, file for file, with a flat CSV of the index for the viewer. Figures change only when a source publishes a dated release; a release listed as published never changes. Cite a release, not the live files.

What is in it

index.csv and index.json: 32 rows, one per model and GPU, each the configuration with the lowest energy per output token whose median inter-token latency stays under 500 ms (workload: text-conversation).
models/: every measured configuration for each of the 18 models (concurrency, parallelism, latency, power), with the best row per GPU.
grid.json: grid emissions intensity in gCO2e per kWh for 194 countries and regions plus the world figure (2024 annual).
hardware.json: GPU reference data (7 parts, 24 MIG profiles) transcribed from manufacturer documents.
per-query.json: 22 rows of energy per query from the AI Energy Score (December 2025).
measurements.json: rows contributed under the measurement protocol (0 at this release); never merged into the index.

Reading a row

The unit is Wh per million output tokens: the GPU energy a server drew while generating one million output tokens, at steady state, in the configuration named by the row. Selection rule, verbatim from the API: per model and GPU, the configuration with the lowest energy per token whose median inter-token latency does not exceed itl_deadline_ms (the leaderboard's default ceiling; no measured configuration reaches it).

At this release the index runs from 13.0 Wh/M (Llama 3.1 8B Instruct, 1 x B200) to 572.9 Wh/M (Llama 3.1 405B Instruct FP8, 8 x H100), a span of 44x. The reference row, used on the site for every comparison, is Qwen 3 32B, 2 x H100 at 63.5 Wh/M.

Boundary

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. The boundary field on every document reads gpu_only.

Carbon

Multiply watt-hours per million output tokens by the grid's gCO2e per kWh and divide by one thousand to get kilograms of CO2e per billion output tokens. grid.json carries the intensity for each area; the world average at this release is 473 gCO2e per kWh. The site's per-country pages apply exactly this arithmetic.

Files

File	What it holds
`manifest.json`	Sources, versions, retrieval dates, licences and this endpoint list.
`index.json`	The index: 32 rows, one per model and GPU, energy-optimal under the latency limit, sorted ascending.
`index.csv`	The index rows as CSV, for a spreadsheet.
`models.json`	Every model on the index with its slug, Hugging Face id, parameter counts and measured GPUs.
`grid.json`	Grid emissions intensity, gCO2e per kWh, 2024 annual average, 193 countries plus World.
`hardware.json`	Manufacturer power ceilings, memory, MIG slice profiles and one embodied-carbon figure.
`per-query.json`	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).
`measurements.json`	Contributed measurements accepted under the protocol, with contributor, affiliation, date, versions and MIG profile; never merged into index.json.
`changes.json`	Every change to the published data or its description: date, summary, documents, reason and source version.
`openapi.json`	OpenAPI 3.1 description of these endpoints.
`models/<slug>.json` (18 files)	One model: every measured configuration (concurrency, parallelism, latency, power) and the best row per GPU.
`schema/measurement.schema.json`	The JSON schema a contributed measurement must satisfy (protocol 0.2).
`NOTICE.md`	Per-source provenance and terms for every data snapshot the index is computed from.

Sources and licences

Source	Version	Retrieved	Terms
ML.ENERGY Leaderboard	3.0 (released 2025-12-01); data compiled 2026-02-16	2026-09-26	Apache-2.0 per the ml-energy/benchmark-v3 dataset card
Ember, Yearly electricity data	October 2026 release (file last modified 2026-10-06)	2026-10-17	CC-BY-4.0
NVIDIA Multi-Instance GPU User Guide, Supported MIG Profiles	last updated 2026-09-11	2026-09-26	manufacturer specifications transcribed as facts; NVIDIA retains its rights in the source documents
NVIDIA product specifications: A100, H100, H200, DGX B200	as published on the retrieval date	2026-09-26	manufacturer specifications transcribed as facts; NVIDIA retains its rights in the source documents
NVIDIA, Product Carbon Footprint Summary for NVIDIA HGX B200	July 2025	2026-09-26	manufacturer specifications transcribed as facts; NVIDIA retains its rights in the source documents
AI Energy Score leaderboard	December 2025 round	2026-09-26	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
OpenAI, What are tokens and how to count them	as published on the retrieval date	2026-09-26	cited, not redistributed

Compilation and site text: CC BY 4.0. Code: Apache 2.0. Third-party figures remain subject to their sources' terms, listed per source.

Citation

Attribution line to copy (fill the Changes field): 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].

The site root carries a CITATION.cff; GitHub renders it as "Cite this repository". The concept DOI https://doi.org/10.5281/zenodo.23478909 always resolves to the latest release.

Latest change

2026-10-19: Launch. Three blog posts: how much energy an AI query uses, what watt-hours per million tokens means, and why an idle GPU wastes energy. The site now serves from GitHub Pages at leadgreen.ai. No change to the data or its description. The full record is in changes.json.

Elsewhere

Website and documentation: https://leadgreen.ai/
JSON API (GET only, CORS open): https://leadgreen.ai/api/v1/ (https://leadgreen.ai/api/v1/manifest.json lists every endpoint)
MCP server for assistants: https://leadgreen.ai/mcp/
Dated releases, each frozen once published: https://leadgreen.ai/api/v1/releases/index.json
Source, build script and issue tracker: https://github.com/katonic-dev/leadgreen-ai
Concept DOI (always resolves to the latest release): https://doi.org/10.5281/zenodo.23478909
Wikidata item: https://www.wikidata.org/wiki/Q141778099

Publisher and funder

Lead Green is run and paid for by Katonic, which sells a sovereign AI platform and benefits when buyers ask the questions on this site. It takes no money from hardware, cloud or model vendors.

Publisher: Katonic AI Technology Ltd, registration CL9944, Dubai International Financial Centre (DIFC), United Arab Emirates, trading this project as Lead Green. Corrections and contributions: hello@leadgreen.ai, answered within 10 working days. Contributed measurements are made under the protocol described at https://leadgreen.ai/contribute/.

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