# Datasets, Page 115 of 132
Source: https://savrn.com/datasets/page/115
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

### [hsi-metal-scrap](https://savrn.com/datasets/hsi-metal-scrap)

[Vivek Kumar](https://savrn.com/model-publishers/vivekkol)

Publicly accessible

[View dataset](https://savrn.com/datasets/hsi-metal-scrap)

Dataset · Text generation

### [huatuo_encyclopedia_qa](https://savrn.com/datasets/huatuo-encyclopedia-qa)

[WongWuwu](https://savrn.com/model-publishers/wuwu616)

This dataset has a total of 364,420 pieces of medical QA data, some of which have multiple questions in different ways. We extract medical QA pairs from plain texts (e.g., medical encyclopedias and medical articles). We collected 8,699 encyclopedia entries for diseases and 2,736 encyclopedia entries for medicines on Chinese Wikipedia. Moreover, we crawled 226,432 high-quality medical articles from the Qianwen Health website. https://zh.wikipedia.org/wiki/ https://51zyzy.com/

Publicly accessible apache-2.0 100K<n<1M

[View dataset](https://savrn.com/datasets/huatuo-encyclopedia-qa)

Dataset

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

[Xin Fang](https://savrn.com/model-publishers/fancy0621)

Publicly accessible

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

Dataset · Tabular classification

### [hurricanes-news-2019-2026](https://savrn.com/datasets/hurricanes-news-2019-2026)

[Fliar BI](https://savrn.com/model-publishers/fliarbi)

A dataset of news about hurricanes and other tropical cyclones (typhoons, cyclones and tropical storms) from a global news index, January 2019 – July 2026, built with the Prism News API (historical and real-time news, https://radar-api.fliarbi.com/news). Every row is one news article about a tropical cyclone, with its date, location, tone, machine-assigned priority, themes and the organisations it names, plus a link that returns the full article through the API. - 49.84% of each year's tropical-cyclone coverage falls in August–October (2019–2025 average). - October 2024 (Hurricane Milton, after Hurricane Helene) was the peak month: 102.1 articles per 10,000 news articles. - The 2025 share…

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

[View dataset](https://savrn.com/datasets/hurricanes-news-2019-2026)

Dataset

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

[Thanh](https://savrn.com/model-publishers/pepernamek)

Publicly accessible

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

Dataset

### [hy4-researchbench-bundle-nim](https://savrn.com/datasets/hy4-researchbench-bundle-nim)

[Jack Klee](https://savrn.com/model-publishers/umannedice)

Publicly accessible

[View dataset](https://savrn.com/datasets/hy4-researchbench-bundle-nim)

Dataset

### [hydra-movies-1](https://savrn.com/datasets/hydra-movies-1)

[Ajith](https://savrn.com/model-publishers/akthereddragon)

Publicly accessible

[View dataset](https://savrn.com/datasets/hydra-movies-1)

Dataset

### [ib-4chan-gemma-3k-datapoints](https://savrn.com/datasets/ib-4chan-gemma-3k-datapoints)

[Florence Germaine](https://savrn.com/model-publishers/massfilegrow)

Publicly accessible

[View dataset](https://savrn.com/datasets/ib-4chan-gemma-3k-datapoints)

Dataset

### [idm_ft_new](https://savrn.com/datasets/idm-ft-new)

[Feng Xiaoxu](https://savrn.com/model-publishers/frozenangel)

Publicly accessible

[View dataset](https://savrn.com/datasets/idm-ft-new)

Dataset

### [illegal_dumps_croatia_ortophoto](https://savrn.com/datasets/illegal-dumps-croatia-ortophoto)

[Silvio Sopic](https://savrn.com/model-publishers/silvios)

Publicly accessible

[View dataset](https://savrn.com/datasets/illegal-dumps-croatia-ortophoto)

Dataset · Question answering

### [imabari_wiki_qa_v4_reasoning_effort_llmjp4](https://savrn.com/datasets/imabari-wiki-qa-v4-reasoning-effort-llmjp4)

[Ikedachin](https://savrn.com/model-publishers/ikedachin)

日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。 This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is regenerated with context from the source articles. 本カードは LLM-jp 4 用のチャットテンプレートに合わせたデータ形式を説明します。思考文の生成モデルは、両形式とも Qwen3.8-27B-NVFP4 です。Qwen3.8版とLLM-jp 4版は、同じ質問・回答・生成済み思考文を共有し、メッセージのフィールド、effortラベル、トークン計数用トークナイザーが異なります。 This card describes the data format adapted to the LLM-jp 4 chat template. Both versions use…

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

[View dataset](https://savrn.com/datasets/imabari-wiki-qa-v4-reasoning-effort-llmjp4)

Dataset · Question answering

### [imabari_wiki_qa_v4_reasoning_effort_qwen38](https://savrn.com/datasets/imabari-wiki-qa-v4-reasoning-effort-qwen38)

[Ikedachin](https://savrn.com/model-publishers/ikedachin)

日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。 This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is regenerated with context from the source articles. 本カードは Qwen3.8 用のチャットテンプレートに合わせたデータ形式を説明します。思考文の生成モデルは、両形式とも Qwen3.8-27B-NVFP4 です。Qwen3.8版とLLM-jp 4版は、同じ質問・回答・生成済み思考文を共有し、メッセージのフィールド、effortラベル、トークン計数用トークナイザーが異なります。 This card describes the data format adapted to the Qwen3.8 chat template. Both versions use…

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

[View dataset](https://savrn.com/datasets/imabari-wiki-qa-v4-reasoning-effort-qwen38)

Dataset

### [image-depth-dataset](https://savrn.com/datasets/image-depth-dataset)

[George A. Mitchell](https://savrn.com/model-publishers/gamitchell)

Preparation notes and schema examples for Music tasks using Image Depth data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - preprocess.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 mit

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

Dataset

### [image-depth-dataset-2024](https://savrn.com/datasets/image-depth-dataset-2024)

[Joao P. Oliveira](https://savrn.com/model-publishers/jzoliveira)

This repository contains a preparation pipeline and a small metadata sample for NLP Summarization work with Image Depth inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated. - clean.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…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/image-depth-dataset-2024)

Dataset

### [image-depth-samples](https://savrn.com/datasets/image-depth-samples)

[David Lopez](https://savrn.com/model-publishers/dalopez2)

Preparation notes and schema examples for Robotics tasks using Image Depth data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - preprocess.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…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/image-depth-samples)

Dataset

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

[Heitor Lopes](https://savrn.com/model-publishers/heitorlopes)

This data card accompanies a lightweight Education loader for Image Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation. - prepare.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 mit

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

Dataset

### [image-translator-pairs](https://savrn.com/datasets/image-translator-pairs)

[Yuvraj Sharma](https://savrn.com/model-publishers/ysharma)

Publicly accessible

[View dataset](https://savrn.com/datasets/image-translator-pairs)

Dataset

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

[Z](https://savrn.com/model-publishers/rhdhf)

Publicly accessible

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

Dataset · Image to 3d

### [img_full_gt](https://savrn.com/datasets/img-full-gt)

[Yao Mingrui](https://savrn.com/model-publishers/add2333)

Paired RGB images, camera parameters, and complete camera-frustum point clouds. The point clouds contain all available source-mesh vertices inside each image's camera frustum, including vertices hidden behind visible surfaces. They are variable-length XYZ arrays in the OpenCV camera frame (x right, y down, z forward). No fixed point count, sampling, normalization, or patch assignment is applied to the stored targets. - assetsmanifest.json: one record per image, with relative paths, point count, coordinate frame, image derivation modes, and SHA256 hashes. - shards/imgfullgtXXXX.tar: independent archives containing complete sample files, grouped by scene. Each archive is at most 5,000,000,000…

Publicly accessible

[View dataset](https://savrn.com/datasets/img-full-gt)

Dataset

### [imgbed](https://savrn.com/datasets/imgbed-4)

[Timo Zhou](https://savrn.com/model-publishers/timozhou1024)

Publicly accessible

[View dataset](https://savrn.com/datasets/imgbed-4)

Dataset

### [indic-asr-staging-shard13](https://savrn.com/datasets/indic-asr-staging-shard13)

[SAJAL MADAN](https://savrn.com/model-publishers/sajalmadan09)

Publicly accessible

[View dataset](https://savrn.com/datasets/indic-asr-staging-shard13)

Dataset

### [indic-asr-staging-shard8](https://savrn.com/datasets/indic-asr-staging-shard8)

[SAJAL MADAN](https://savrn.com/model-publishers/sajalmadan09)

Publicly accessible

[View dataset](https://savrn.com/datasets/indic-asr-staging-shard8)

Dataset

### [industrial-text-tabular-clean-2023](https://savrn.com/datasets/industrial-text-tabular-clean-2023)

[Yijungkook](https://savrn.com/model-publishers/yijungkook)

Preparation notes and schema examples for Industrial tasks using Text Tabular data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - dataloader.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…

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/industrial-text-tabular-clean-2023)

Dataset

### [insert_carrot_into_the_hole_hil_v2_r1](https://savrn.com/datasets/insert-carrot-into-the-hole-hil-v2-r1)

[Ai](https://savrn.com/model-publishers/alvinai)

Publicly accessible

[View dataset](https://savrn.com/datasets/insert-carrot-into-the-hole-hil-v2-r1)

## 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.