# Datasets, Page 69 of 132
Source: https://savrn.com/datasets/page/69
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 · Text retrieval

### [ipfs_korea_laws_ir](https://savrn.com/datasets/ipfs-korea-laws-ir)

[Justicedao](https://savrn.com/model-publishers/justicedao)

Research retrieval release of endomorphosis/ipfskorealaws (revision a174ce3d2e51385dd21584ba4aa7618c97910d86) packaged as country-laws-ir-graphrag/v1 (layout family skillcenter-huggingface-release/v3 / publicus-ir). Not legal advice. This is a research snapshot. The official gazette / authentic source of Korea (ROK) prevails over this corpus. Retrieved documents and graph edges are retrieval evidence only. No legal text was invented. Primary key: entrycid (CIDv1 raw sha2-256 of a canonical identity record). Integer documentindex values are compact shard pointers, not identities. Target Hub id (packaging metadata only): justicedao/ipfskorealawsir. entrycid, lawcid, recordtype, jurisdiction…

Publicly accessible other 100K<n<1M

[View dataset](https://savrn.com/datasets/ipfs-korea-laws-ir)

Dataset

### [promptlane-what-raw](https://savrn.com/datasets/promptlane-what-raw)

[Dingyi](https://savrn.com/model-publishers/dingyi222666)

Publicly accessible

[View dataset](https://savrn.com/datasets/promptlane-what-raw)

Dataset

### [Our1-2b-Dataset](https://savrn.com/datasets/our1-2b-dataset)

[Ouroboros Research](https://savrn.com/model-publishers/ouroboros-research)

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/our1-2b-dataset)

Dataset

### [ipfs_brazil_municipal_laws](https://savrn.com/datasets/ipfs-brazil-municipal-laws)

[Benjamin J Barber](https://savrn.com/model-publishers/endomorphosis)

Municipal law of Brazil, harvested from SAPL (Sistema de Apoio ao Processo Legislativo, Interlegis / Senado Federal) instances run by camaras municipais. Part of the world municipal-law coverage programme (one repo per country, html.parquet per jurisdiction, shared worldmunicipal schema). (21.3%); this dataset is being filled tranche by tranche from that census. of 2025 only. (0 full-2025 harvests + 4 pilot seed files). Each SAPL instance exposes a Normas Juridicas search (/norma/pesquisar). Per law, the structured texto articulado (article-level HTML) is taken where the instance provides it; otherwise the official original-text PDF is extracted as one document-level row. Instance content…

Publicly accessible

[View dataset](https://savrn.com/datasets/ipfs-brazil-municipal-laws)

Dataset

### [fineweb-edu-ga-20261007-160340](https://savrn.com/datasets/fineweb-edu-ga-20261007-160340)

[Daniel Gallagher](https://savrn.com/model-publishers/danielgallagherire)

Publicly accessible

[View dataset](https://savrn.com/datasets/fineweb-edu-ga-20261007-160340)

Dataset

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

[Boun Mee](https://savrn.com/model-publishers/laosbustickets)

Open data from Laos Bus Tickets: border checkpoints, bus, train and flight routes, the Laos–China Railway timetable and the Laos transport map. laosbustickets.com is the original source; this repository is a copy, synced every day by a GitHub Action. If the two ever differ, trust the website, and please cite its pages. - checkedAt: when the timetable was last read from the railway's app (UTC). If a daily check fails, the previous timetable is kept, so this date tells you how fresh it is. - dates: the days covered (the app sells tickets 7 days ahead). - trains[]: train (D88, C95, K11…), direction, from, to, runsOn (dates), weekdays, weekdaysNotOnSaleYet (days the app has not opened yet, not…

Publicly accessible cc-by-4.0

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

Dataset

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

[Nicholas Clark Ramirez](https://savrn.com/model-publishers/nicholasramirez92267)

Publicly accessible

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

Dataset

### [verilog_data-1-C](https://savrn.com/datasets/verilog-data-1-c)

[SAIFI INDUSTRIES](https://savrn.com/model-publishers/saifiindustries)

Publicly accessible

[View dataset](https://savrn.com/datasets/verilog-data-1-c)

Dataset

### [world_of_warcraft](https://savrn.com/datasets/world-of-warcraft)

[Opensima08](https://savrn.com/model-publishers/opensima08)

Access requested at publisher

[View dataset](https://savrn.com/datasets/world-of-warcraft)

Dataset · Object detection

### [polyp-regime-bench-data](https://savrn.com/datasets/polyp-regime-bench-data)

[AI-for-Medicine-and-Health](https://savrn.com/model-publishers/ai-for-medicine-and-health)

This repository defines the exact image sets used in PolypRegimeBench, a controlled benchmark of 12 polyp detectors across 6 training regimes. It contains manifests only: file lists, split assignments, filtered IDs, augmentation recipes and checksums. It does not redistribute any third-party endoscopy images, masks or videos. White-light images only. Narrow-band images were excluded so that modality is not confounded with the other factors. Split seed 42, fixed for all runs. Test sets are almost entirely polyp-positive (0, 5 and 0 negative images respectively). The augmentations transform image and mask together: flips, affine (scale, translation, rotation, shear), blur, colour/contrast and…

Publicly accessible

[View dataset](https://savrn.com/datasets/polyp-regime-bench-data)

Dataset

### [Results_Multimodal_Feature_Fusion_DL](https://savrn.com/datasets/results-multimodal-feature-fusion-dl)

[Hoang Phi Hung](https://savrn.com/model-publishers/hoangphihung442004)

Publicly accessible

[View dataset](https://savrn.com/datasets/results-multimodal-feature-fusion-dl)

Dataset

### [backup-service](https://savrn.com/datasets/backup-service)

[Albert Lin](https://savrn.com/model-publishers/albertkuilin)

Access requested at publisher

[View dataset](https://savrn.com/datasets/backup-service)

Dataset · Text generation

### [tb21-qwen3.8-27b-terminus2](https://savrn.com/datasets/tb21-qwen3-8-27b-terminus2)

[OpenGuardrails](https://savrn.com/model-publishers/openguardrails)

Raw, unedited agent trajectories for three full runs over the same 89 Terminal-Bench 2.1 tasks, plus every remediation re-run, so each number below can be checked against what the agents actually did. The gaps are not statistically significant. Paired over the same tasks: Qwen vs DeepSeek+terminus-2 is net +6 (exact McNemar p=0.29); Qwen vs DeepSeek+dsh is net +3 (p=0.68). At 89 tasks and one attempt each, a lead this size is what run-to-run noise produces. Read the rows as "comparable", not as a ranking. 1. Start from what each run actually scored. 2. Trials lost to infrastructure were re-run under the repaired environment; the re-run's verdict replaces the original. 3. A trial whose work…

Publicly accessible mit n<1K

[View dataset](https://savrn.com/datasets/tb21-qwen3-8-27b-terminus2)

Dataset

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

[Aa](https://savrn.com/model-publishers/deeznull)

Transcipts of @Axiomism youtube channel contents in various languages Axiomism youtube kanalinin video, shorts ve yayinlarinin cesitli dillerde transkriptleri Translations are made by the latest best AI available to the public, original language is Turkish https://www.youtube.com/watch?v=4DtePT40AVo Please add new videos/shorts/streams transcripts when new ones uploaded Can add new translations but please use best AI available to do so Created with the consent of @Axiomism

Access requested at publisher cc0-1.0 10K<n<100K

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

Dataset

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

[Gisfun](https://savrn.com/model-publishers/gisfun)

Publicly accessible

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

Dataset

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

[Ly Hong Thanh](https://savrn.com/model-publishers/thanhly17934)

Publicly accessible

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

Dataset · Text retrieval

### [ipfs_belgium_laws_ir](https://savrn.com/datasets/ipfs-belgium-laws-ir)

[Justicedao](https://savrn.com/model-publishers/justicedao)

Research retrieval release of endomorphosis/ipfsbelgiumlaws (revision ) packaged as country-laws-ir-graphrag/v1 (layout family skillcenter-huggingface-release/v3 / publicus-ir). Not legal advice. This is a research snapshot. The official gazette / authentic source of Belgium prevails over this corpus. Retrieved documents and graph edges are retrieval evidence only. No legal text was invented. Primary key: entrycid (CIDv1 raw sha2-256 of a canonical identity record). Integer documentindex values are compact shard pointers, not identities. Target Hub id (packaging metadata only): justicedao/ipfsbelgiumlawsir. entrycid, lawcid, recordtype, jurisdiction, language, instrumentid, instrumenttitle…

Publicly accessible other n<1K

[View dataset](https://savrn.com/datasets/ipfs-belgium-laws-ir)

Dataset

### [store-d7dac4da](https://savrn.com/datasets/store-d7dac4da)

[Matthew Roy](https://savrn.com/model-publishers/msrcam)

Opaque archive shards.

Access requested at publisher other

[View dataset](https://savrn.com/datasets/store-d7dac4da)

Dataset · Robotics

### [EgoDex-Retarget-16robot](https://savrn.com/datasets/egodex-retarget-16robot)

[VR-VLA](https://savrn.com/model-publishers/vr-vla)

EgoDex Retarget — 16 robots EgoDex egocentric human demonstrations retargeted onto 16 robot embodiments. For every episode, the human wrist and hand motion is solved into a joint trajectory for each robot, and the robot is rendered and composited into the original scene, over hand-erased frames and per-frame metric depth. Each (robot, task) pair is one LeRobot v3.0 dataset. VR-VLA/VR-egodex-inpaint-cache-full (hand-erased frames), VR-VLA/VR-egodex-depth-cache-full

Publicly accessible other

[View dataset](https://savrn.com/datasets/egodex-retarget-16robot)

Dataset

### [tony_nnue](https://savrn.com/datasets/tony-nnue)

[Çetin](https://savrn.com/model-publishers/cturan)

The data here seems good enough on its own to train a pretty strong NNUE. In my own tests it did a good job of pulling draws out of Stockfish:P To keep costs down, it was relabeled with t1-256x10-rl-base-swa-3860000, and most of the self-play games were also generated with that same model, in a single pass with a bit of temperature. Bigger lc0 models would be far more effective, but this one hit around 660k/s on 8× 5090s, which made it cheap, so that's what I went with.

Publicly accessible odc-by

[View dataset](https://savrn.com/datasets/tony-nnue)

Dataset · Text to image

### [MGFlow-T2I](https://savrn.com/datasets/mgflow-t2i)

[Haoyang Shi](https://savrn.com/model-publishers/shy0423)

Synthetic image-prompt pairs used as the reference distribution for MGFlow text-to-image post-training in The same reference images support both image-only and joint image-text matching. Model checkpoints and fitted reference distributions: The dataset contains 301,706 synthetic images at 512×512 with English prompts. All images were generated with the official distilled [FLUX.2 [klein] 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B) model using four sampling steps and guidance scale 1. Reference construction follows the procedure used by iRDM and AMFD: generate 24 candidates per caption, and retain the three with the highest PickScore. GenEval correctness test, considering up…

Publicly accessible 100K<n<1M

[View dataset](https://savrn.com/datasets/mgflow-t2i)

Dataset · Question answering

### [TRIAGE_Bench](https://savrn.com/datasets/triage-bench)

[Hou Wan Long](https://savrn.com/model-publishers/darrenloong)

TRIAGE-Bench is a benchmark for evaluating how LLMs resolve conflicts between authoritative clinical knowledge sources. It covers 2,000 items across four conflict types, each with an explicit governing policy that defines policy-consistent correctness. - 2,000 benchmark items (500 per conflict type) grounded in real guideline and drug-label discrepancies - 2x2 factorial evaluation: Hint x Source design (NS, NS+Hint, SP, SP+Hint) - 56,000 evaluation records across 7 frontier LLMs - 400 expert-validated subset (TRIAGE-Bench-Verified) The 2x2 factorial design crosses two factors: GPT-5.6, Claude-Opus-4.8, GLM-5.2, Qwen-3.7, MiniMax-M3, DeepSeek-V4-Pro, Kimi-K2.6 1. Source dominates: Providing…

Publicly accessible cc-by-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/triage-bench)

Dataset

### [ai-news-corpus](https://savrn.com/datasets/ai-news-corpus)

[Shanudengre82](https://savrn.com/model-publishers/shanudengre82)

Publicly accessible

[View dataset](https://savrn.com/datasets/ai-news-corpus)

Dataset

### [guardrail-fewshot-checkpoints](https://savrn.com/datasets/guardrail-fewshot-checkpoints)

[Ahmed Alsebaei](https://savrn.com/model-publishers/ahmed21345)

Access requested at publisher

[View dataset](https://savrn.com/datasets/guardrail-fewshot-checkpoints)

## 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.