Pith Number

pith:EZFLDGAM

pith:2026:EZFLDGAMR7AVUSODBDGS4QLUYT

not attested not anchored not stored refs pending

Hugging Carbon: Quantifying the Training Carbon Emissions of AI Models at Scale

Jing Qiu, Jinjin Gu, Junhua Zhao, Ruibo Ming, Xinlei Wang

A FLOPs-based framework with tiered metadata handling estimates that training popular open-source models on Hugging Face has emitted approximately 58,000 metric tons of carbon and introduces the ATCI metric for training efficiency.

arxiv:2605.01549 v2 · 2026-05-02 · cs.CY

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\usepackage{pith}
\pithnumber{EZFLDGAMR7AVUSODBDGS4QLUYT}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our results show that training the most popular open-source models (with over 5,000 downloads) has resulted in approximately 5.8×10^4 metric tons of carbon emissions.

C2weakest assumption

Given that the Hugging Face (HF) platform well represents the broader open-source community, we treat it as a large-scale, publicly accessible, and audit-ready corpus for carbon accounting.

C3one line summary

Cited by

1 paper in Pith

How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification

Receipt and verification

First computed	2026-07-03T01:17:55.630584Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

264ab1980c8fc15a49c308cd2e4174c4c83a33359e94b1f213f9d0f3b231446f

Aliases

arxiv: 2605.01549 · arxiv_version: 2605.01549v2 · doi: 10.48550/arxiv.2605.01549 · pith_short_12: EZFLDGAMR7AV · pith_short_16: EZFLDGAMR7AVUSOD · pith_short_8: EZFLDGAM

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EZFLDGAMR7AVUSODBDGS4QLUYT \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 264ab1980c8fc15a49c308cd2e4174c4c83a33359e94b1f213f9d0f3b231446f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6ca7ce928850f845633da69f2d726d913da8d6abed54569adf4583bf2eb9b6d7",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2026-05-02T17:32:56Z",
    "title_canon_sha256": "509bd7a03ef987d3c03053f64d1819d8e15958bb0c86978310701a8aa5284452"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.01549",
    "kind": "arxiv",
    "version": 2
  }
}