# Datasets, Page 128 of 132
Source: https://savrn.com/datasets/page/128
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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SAVRN Model Hub

# AI Training Datasets

Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.

11,122Models

3,164Datasets

285Papers

6,792Publishers

11,852Sourced relationships

Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Datasets

Task

- [Text generation127](https://savrn.com/datasets?task=Text+generation)
- [Robotics106](https://savrn.com/datasets?task=Robotics)
- [Other48](https://savrn.com/datasets?task=Other)
- [Text classification47](https://savrn.com/datasets?task=Text+classification)
- [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval)
- [Question answering36](https://savrn.com/datasets?task=Question+answering)
- [Speech recognition33](https://savrn.com/datasets?task=Speech+recognition)
- [Tabular classification21](https://savrn.com/datasets?task=Tabular+classification)
- [Time series forecasting21](https://savrn.com/datasets?task=Time+series+forecasting)
- [Image to text17](https://savrn.com/datasets?task=Image+to+text)
- [Image classification16](https://savrn.com/datasets?task=Image+classification)
- [Object detection14](https://savrn.com/datasets?task=Object+detection)
- [Text to speech14](https://savrn.com/datasets?task=Text+to+speech)
- [Visual question answering14](https://savrn.com/datasets?task=Visual+question+answering)
- [Image to image11](https://savrn.com/datasets?task=Image+to+image)
- [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation)
- [Image to 3d9](https://savrn.com/datasets?task=Image+to+3d)
- [Translation9](https://savrn.com/datasets?task=Translation)
- [Video classification9](https://savrn.com/datasets?task=Video+classification)
- [Audio classification7](https://savrn.com/datasets?task=Audio+classification)
- [Feature extraction7](https://savrn.com/datasets?task=Feature+extraction)
- [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning)
- [Text to image7](https://savrn.com/datasets?task=Text+to+image)
- [Text to video7](https://savrn.com/datasets?task=Text+to+video)

License

- [other250](https://savrn.com/datasets?license=other)
- [apache-2.0236](https://savrn.com/datasets?license=apache-2.0)
- [mit232](https://savrn.com/datasets?license=mit)
- [cc-by-4.0224](https://savrn.com/datasets?license=cc-by-4.0)
- [cc-by-nc-4.077](https://savrn.com/datasets?license=cc-by-nc-4.0)
- [cc0-1.030](https://savrn.com/datasets?license=cc0-1.0)
- [cc-by-sa-4.029](https://savrn.com/datasets?license=cc-by-sa-4.0)
- [odc-by24](https://savrn.com/datasets?license=odc-by)
- [cc-by-nc-sa-4.020](https://savrn.com/datasets?license=cc-by-nc-sa-4.0)
- [unknown14](https://savrn.com/datasets?license=unknown)
- [cc-by-sa-3.06](https://savrn.com/datasets?license=cc-by-sa-3.0)
- [odbl6](https://savrn.com/datasets?license=odbl)
- [gpl-3.05](https://savrn.com/datasets?license=gpl-3.0)
- [cc-by-3.04](https://savrn.com/datasets?license=cc-by-3.0)
- [cc3](https://savrn.com/datasets?license=cc)
- [cc-by-nc-nd-4.03](https://savrn.com/datasets?license=cc-by-nc-nd-4.0)
- [gpl3](https://savrn.com/datasets?license=gpl)
- [cc-by-2.02](https://savrn.com/datasets?license=cc-by-2.0)
- [cc-by-nd-4.02](https://savrn.com/datasets?license=cc-by-nd-4.0)
- [cdla-sharing-1.02](https://savrn.com/datasets?license=cdla-sharing-1.0)
- [agpl-3.01](https://savrn.com/datasets?license=agpl-3.0)
- [bsl-1.01](https://savrn.com/datasets?license=bsl-1.0)
- [c-uda1](https://savrn.com/datasets?license=c-uda)
- [cc-by-nc-3.01](https://savrn.com/datasets?license=cc-by-nc-3.0)

Access

- [Publicly accessible2,933](https://savrn.com/datasets?access=open_access)
- [Access requested at publisher231](https://savrn.com/datasets?access=gated)

Publisher

- [Benjamin J Barber35](https://savrn.com/datasets?publisher=endomorphosis)
- [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st)
- [Ji Xie23](https://savrn.com/datasets?publisher=sanaka87)
- [Justicedao18](https://savrn.com/datasets?publisher=justicedao)
- [LeDuy13](https://savrn.com/datasets?publisher=duyle2408)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn11)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn12)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn13)
- [National Engineering Research Center for Big Data Software13](https://savrn.com/datasets?publisher=thulab)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn10)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn14)
- [Byte I12](https://savrn.com/datasets?publisher=icdn15)
- [Rebrowser.net12](https://savrn.com/datasets?publisher=rebrowser)
- [Shufan Jiang12](https://savrn.com/datasets?publisher=tsumugii)
- [Ai210](https://savrn.com/datasets?publisher=allenai)
- [Ps Yo10](https://savrn.com/datasets?publisher=atokforps)
- [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind)
- [Deesced9](https://savrn.com/datasets?publisher=ameluce)
- [Jiahao Zhang9](https://savrn.com/datasets?publisher=hi-todayis-jh)
- [Julia Dollis9](https://savrn.com/datasets?publisher=juliadollis)
- [Massive Text Embedding Benchmark9](https://savrn.com/datasets?publisher=mteb)
- [Pranab Pattanaik9](https://savrn.com/datasets?publisher=p2samapa)
- [Sota9](https://savrn.com/datasets?publisher=sotaagi2030)
- [Sunsetcanyon9](https://savrn.com/datasets?publisher=sunsetcanyon)

Size

- [10K<n<100K125](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K100](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M70](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M51](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B21](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B8](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B2](https://savrn.com/datasets?size=10B%3Cn%3C100B)
- [10GB<n<100GB1](https://savrn.com/datasets?size=10GB%3Cn%3C100GB)
- [120,8981](https://savrn.com/datasets?size=120%2C898)
- [1B-tokens1](https://savrn.com/datasets?size=1B-tokens)
- [1T<n<10T1](https://savrn.com/datasets?size=1T%3Cn%3C10T)
- [n1K<n10K1](https://savrn.com/datasets?size=n1K%3Cn10K)
- [n<100K1](https://savrn.com/datasets?size=n%3C100K)
- [n<10K1](https://savrn.com/datasets?size=n%3C10K)
- [n<1GB1](https://savrn.com/datasets?size=n%3C1GB)
- [n>1M1](https://savrn.com/datasets?size=n%3E1M)

Domain

- [LeRobot31](https://savrn.com/datasets?domain=LeRobot)
- [legal, law, graphrag12](https://savrn.com/datasets?domain=legal%2C+law%2C+graphrag)
- [tsfile, timeseries, time-series12](https://savrn.com/datasets?domain=tsfile%2C+timeseries%2C+time-series)
- [open-data, data-science, web-scraping9](https://savrn.com/datasets?domain=open-data%2C+data-science%2C+web-scraping)
- [seo-cluster, web-nodes9](https://savrn.com/datasets?domain=seo-cluster%2C+web-nodes)
- [dpo, rlhf, ultrafeedback8](https://savrn.com/datasets?domain=dpo%2C+rlhf%2C+ultrafeedback)
- [mteb, text, image7](https://savrn.com/datasets?domain=mteb%2C+text%2C+image)
- [nepali, instruction-tuning, agriculture7](https://savrn.com/datasets?domain=nepali%2C+instruction-tuning%2C+agriculture)
- [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance)
- [LeRobot, robotics, lerobot4](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot)
- [ml-intern4](https://savrn.com/datasets?domain=ml-intern)
- [AMASS, Retarget, Robotics3](https://savrn.com/datasets?domain=AMASS%2C+Retarget%2C+Robotics)
- [benchmark, agents, terminal3](https://savrn.com/datasets?domain=benchmark%2C+agents%2C+terminal)
- [climate3](https://savrn.com/datasets?domain=climate)
- [code3](https://savrn.com/datasets?domain=code)
- [crypto, cryptocurrency, finance3](https://savrn.com/datasets?domain=crypto%2C+cryptocurrency%2C+finance)
- [data-attribution, influence-functions, interpretability3](https://savrn.com/datasets?domain=data-attribution%2C+influence-functions%2C+interpretability)
- [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke)
- [video3](https://savrn.com/datasets?domain=video)
- [LeRobot, lerobot, grabette2](https://savrn.com/datasets?domain=LeRobot%2C+lerobot%2C+grabette)
- [LeRobot, ur7e, robotiq2](https://savrn.com/datasets?domain=LeRobot%2C+ur7e%2C+robotiq)
- [agentenv, rl-environment, agents2](https://savrn.com/datasets?domain=agentenv%2C+rl-environment%2C+agents)
- [ai-governance, measurement2](https://savrn.com/datasets?domain=ai-governance%2C+measurement)
- [autonomous, agents, evolution2](https://savrn.com/datasets?domain=autonomous%2C+agents%2C+evolution)

[Most downloaded](https://savrn.com/datasets?sort=downloads)[Most liked](https://savrn.com/datasets?sort=likes)[Recently updated](https://savrn.com/datasets?sort=updated)[Name](https://savrn.com/datasets?sort=name)

3,164 datasets, sorted by most downloaded.

Dataset

### [spatialfuser-data](https://savrn.com/datasets/spatialfuser-data)

[Wenhao Cai](https://savrn.com/model-publishers/rnadycai)

Publicly accessible

[View dataset](https://savrn.com/datasets/spatialfuser-data)

Dataset

### [specieval](https://savrn.com/datasets/specieval)

[Dan Wahl](https://savrn.com/model-publishers/drwahl)

SpeciEval measures LLM attitudes towards animals with validated psychological scales from Hopwood et al. (2025). This dataset holds the questions and every model response behind the SpeciEval leaderboard: 25,380 scored responses from 137 models, plus the raw Inspect logs they come from. Each model rates its agreement with 18 statements about farmed animals on a 7-point scale, from 1 (strongly disagree) to 7 (strongly agree). The statements come from four instruments: A system message gives the scale and the user message asks for step-by-step reasoning before a final ANSWER: line. Each question is asked at least 10 times per model. Twelve questions (inoverallscore) make up the overall…

Publicly accessible cc-by-4.0 10K<n<100K

[View dataset](https://savrn.com/datasets/specieval)

Dataset

### [speech-sensor-fusion](https://savrn.com/datasets/speech-sensor-fusion)

[Daniel P.](https://savrn.com/model-publishers/danielpat)

Preparation notes and schema examples for Speech tasks using Sensor Fusion data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - dataset.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the data…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/speech-sensor-fusion)

Dataset

### [speechlm-pretrain-public-3](https://savrn.com/datasets/speechlm-pretrain-public-3)

[Giahy2507](https://savrn.com/model-publishers/giahy2507)

One config per corpus, each a set of parquet shards whose rows carry the audio of every segment of one pretraining record, so a corpus can be streamed front to back: Rows: id, source, seconds, numsegments, meta (JSON), and segments, a (16-bit FLAC unless the corpus's packinfo.json says otherwise), mono 16 kHz. Rows are pre-shuffled; each corpus has a multiple of 8 shards. Each corpus keeps its own licence -- see /packinfo.json. Built by scripts/packstreaming.py.

Publicly accessible

[View dataset](https://savrn.com/datasets/speechlm-pretrain-public-3)

Dataset · Robotics

### [sponge_task_9_24](https://savrn.com/datasets/sponge-task-9-24)

[Nakanakagawa](https://savrn.com/model-publishers/nakanakagawa)

This dataset was created using LeRobot.

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/sponge-task-9-24)

Dataset

### [srl-poc-shards](https://savrn.com/datasets/srl-poc-shards)

[SRPLB1](https://savrn.com/model-publishers/srplb1)

Access requested at publisher

[View dataset](https://savrn.com/datasets/srl-poc-shards)

Dataset

### [stack-cube-red-yellow](https://savrn.com/datasets/stack-cube-red-yellow)

[zipei ma(SII)](https://savrn.com/model-publishers/xiaoma26)

Publicly accessible

[View dataset](https://savrn.com/datasets/stack-cube-red-yellow)

Dataset · Text generation

### [stack-v3-docs](https://savrn.com/datasets/stack-v3-docs)

[Takamichi Ohara](https://savrn.com/model-publishers/tokinasin)

Exact duplicate samples were removed after extraction. These subsets are derived from The Stack v3 train dataset, which is released under the ODC-By 1.0 license. Individual source files may have different original licenses; their use must comply with those terms, including attribution where applicable. Repository provenance and per-file license metadata (repopath, commitid, licensetype, and detectedlicenses) are preserved in each sample. License detection may be incomplete or inaccurate.

Publicly accessible odc-by

[View dataset](https://savrn.com/datasets/stack-v3-docs)

Dataset

### [steer-cards-gemma3](https://savrn.com/datasets/steer-cards-gemma3)

[Scott](https://savrn.com/model-publishers/aipotek)

What this is. Every text from a pre-registered experiment that pushed 128 features of Google DeepMind's Gemma Scope 2 dictionary into Gemma 3 4B IT, at eight strengths, against two kinds of control, with every text scored blind by two independent AI readers. It answers a practical question for anyone steering with sparse-autoencoder features: which features actually change what the model writes, how hard do you have to push, and what predicts it? Made by the Aipotek project: an instrument for exploring language models by turning their internal "dials". Professionally copy/pasted by Scott, with Claude (Anthropic) writing the code, running the analysis and drafting this card. Everything here…

Publicly accessible cc-by-4.0 10K<n<100K

[View dataset](https://savrn.com/datasets/steer-cards-gemma3)

Dataset

### [stopballright](https://savrn.com/datasets/stopballright)

[Quanxin](https://savrn.com/model-publishers/cdcaffee)

Raw teleoperated robotics episodes collected with a physical Galaxea R1 Lite. - 20260917: 101 episodes Files retain their stable recorder-generated episode identifiers below episodes/YYYYMMDD/. includedmanifest.jsonl is the authoritative file and size inventory. Raw source files were not modified during preparation or upload. The owner explicitly authorized this new public Hugging Face repository and directed the raw upload to proceed without a separate representative-frame review. Camera streams may show the laboratory, operators, screens, labels, or other incidental details. This release has therefore not received an exhaustive frame-by-frame privacy review. Report any privacy or consent…

Publicly accessible

[View dataset](https://savrn.com/datasets/stopballright)

Dataset

### [sudoku](https://savrn.com/datasets/sudoku)

[Quentin Gallouédec](https://savrn.com/model-publishers/qgallouedec)

9x9 Sudoku puzzles, each with a unique solution, rated by the hardest human solving technique they require. Each puzzle is the one with the fewest clues that its difficulty level still solves, along a random clue-removal path. - "puzzle": The 9x9 grid as a list of rows, 0 for empty cells. - "solution": The solved 9x9 grid as a list of rows. - "difficulty": One of easy, medium, hard, extreme. The script used to generate this dataset can be found here.

Publicly accessible

[View dataset](https://savrn.com/datasets/sudoku)

Dataset · Object detection

### [sugar_beet_weed_detection](https://savrn.com/datasets/sugar-beet-weed-detection)

[AgML](https://savrn.com/model-publishers/project-agml)

PaimioSB2023 is a field dataset for detecting sugar beet seedlings and weeds, collected in 2023 on a 2-hectare research sugar beet field in Hevonpää, Finland, where a commercial FarmDroid FD20 robot was evaluated for seeding and weeding. RGB images from a GoPro action camera mounted 2 m above the ground on the robot, a Parrot Anafi drone flying at 4 m, and a mobile phone were cut into 1280 x 1280 tiles, and each tile was manually annotated with bounding boxes for two classes: sugar beet and weed. The sourceimage column identifies the original photo each tile came from, so tiles from the same photo can be kept in the same split. The dataset contains 18,593 images with 338,171 bounding box…

Publicly accessible cc-by-4.0 10K<n<100K

[View dataset](https://savrn.com/datasets/sugar-beet-weed-detection)

Dataset · Text classification

### [superfast-tiny-home-robotic-data](https://savrn.com/datasets/superfast-tiny-home-robotic-data)

[Sr Aivante](https://savrn.com/model-publishers/sraivante)

Versioned training, device-catalog and diagnostic data for Publisher: sraivante. Release date: 2026-09-26. Dataset version: v1.0.0. Instructions in English and Romanized Hindi/Hinglish map to a single structured device intent. The release preserves the raw command source, the recovered model training sample, exact diagnostic inputs, all predictions and the logical device catalog. There is no separate fine-tuning dataset for this count-based model. The test split is a diagnostic, not a held-out generalization benchmark. Training and diagnostic record-content overlap is also 82. There is no separate held-out validation set and no independently collected real-world test set. Configurations…

Publicly accessible apache-2.0 1M<n<10M

[View dataset](https://savrn.com/datasets/superfast-tiny-home-robotic-data)

Dataset

### [surfPRM-data](https://savrn.com/datasets/surfprm-data)

[PoloWitty](https://savrn.com/model-publishers/polowitty)

Publicly accessible

[View dataset](https://savrn.com/datasets/surfprm-data)

Dataset

### [swe-agent-subset-selection-vectors](https://savrn.com/datasets/swe-agent-subset-selection-vectors)

[Mahmoud Ayyad](https://savrn.com/model-publishers/mahmoud-queens)

Publicly accessible

[View dataset](https://savrn.com/datasets/swe-agent-subset-selection-vectors)

Dataset · Text generation

### [syed-med-medical-qa-synthetic-4k](https://savrn.com/datasets/syed-med-medical-qa-synthetic-4k)

[Syed Masood Haji Ahmedyaseen](https://savrn.com/model-publishers/zaiath)

Locally generated synthetic medical question/answer rows, produced with a local DeepSeek-R1 model (via Ollama, $0, no API calls) and used — together with 7 public datasets — to fine-tune 3,002 + 801 + 401 = 4,204 rows — the synthetic share of the 196,846-row SFT set. Every row is one JSON object using one template — the same one the model was trained on (there is no separate system/schema to guess): Whitespace is normalised. Rows carry no end-of-turn token on purpose: the model was trained to stop at the next ### marker and the demo app enforces that too. 1. a topic matrix (condition × country × age group) drove a local DeepSeek-R1 model; 2. every row was validated (template match…

Publicly accessible other 1K<n<10K

[View dataset](https://savrn.com/datasets/syed-med-medical-qa-synthetic-4k)

Dataset

### [synapse-titan-30b](https://savrn.com/datasets/synapse-titan-30b)

[Aman Mandiya](https://savrn.com/model-publishers/mandiyaaman)

Publicly accessible

[View dataset](https://savrn.com/datasets/synapse-titan-30b)

Dataset

### [synthetic-transit-routing-ko](https://savrn.com/datasets/synthetic-transit-routing-ko)

[Quasarlap](https://savrn.com/model-publishers/quasarlap)

Publicly accessible

[View dataset](https://savrn.com/datasets/synthetic-transit-routing-ko)

Dataset

### [t2i-eval-toolkit-assets](https://savrn.com/datasets/t2i-eval-toolkit-assets)

[Liangzhi Li](https://savrn.com/model-publishers/liangzhi-li)

Publicly accessible

[View dataset](https://savrn.com/datasets/t2i-eval-toolkit-assets)

Dataset

### [t2i-raw-data-p000](https://savrn.com/datasets/t2i-raw-data-p000)

[Ymhlakytt](https://savrn.com/model-publishers/ymhlakytt)

Publicly accessible

[View dataset](https://savrn.com/datasets/t2i-raw-data-p000)

Dataset

### [tb2-t4e1ta-ctl](https://savrn.com/datasets/tb2-t4e1ta-ctl)

[Shane Boulay](https://savrn.com/model-publishers/tinkersnot)

Publicly accessible

[View dataset](https://savrn.com/datasets/tb2-t4e1ta-ctl)

Dataset

### [tb2-t4e2xb-ctl](https://savrn.com/datasets/tb2-t4e2xb-ctl)

[Shane Boulay](https://savrn.com/model-publishers/tinkersnot)

Publicly accessible

[View dataset](https://savrn.com/datasets/tb2-t4e2xb-ctl)

Dataset

### [tb2-t4eael-ctl](https://savrn.com/datasets/tb2-t4eael-ctl)

[Shane Boulay](https://savrn.com/model-publishers/tinkersnot)

Publicly accessible

[View dataset](https://savrn.com/datasets/tb2-t4eael-ctl)

Dataset

### [tb2-t4pjl2-ctl](https://savrn.com/datasets/tb2-t4pjl2-ctl)

[Shane Boulay](https://savrn.com/model-publishers/tinkersnot)

Publicly accessible

[View dataset](https://savrn.com/datasets/tb2-t4pjl2-ctl)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval)

Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator)

Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## Related SAVRN Research

The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.

SAVRN Index

### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.