# Models, Page 227 of 512
Source: https://savrn.com/models/page/227
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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SAVRN Model Hub

# Open-Weight Models

An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.

Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.

12,283Models

3,510Datasets

290Papers

7,436Publishers

12,632Sourced relationships

Updated 2026-10-22 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,506](https://savrn.com/models?task=Text+generation)
- [Image and text to text560](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction396](https://savrn.com/models?task=Feature+extraction)
- [Robotics379](https://savrn.com/models?task=Robotics)
- [Text classification328](https://savrn.com/models?task=Text+classification)
- [Text to image228](https://savrn.com/models?task=Text+to+image)
- [Speech recognition185](https://savrn.com/models?task=Speech+recognition)
- [Text to speech143](https://savrn.com/models?task=Text+to+speech)
- [Image classification139](https://savrn.com/models?task=Image+classification)
- [Audio classification130](https://savrn.com/models?task=Audio+classification)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Token classification99](https://savrn.com/models?task=Token+classification)
- [Reinforcement learning97](https://savrn.com/models?task=Reinforcement+learning)
- [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation90](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [Translation79](https://savrn.com/models?task=Translation)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask69](https://savrn.com/models?task=Fill+mask)
- [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting)
- [Video classification60](https://savrn.com/models?task=Video+classification)
- [Image to text59](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,947](https://savrn.com/models?library=transformers)
- [peft361](https://savrn.com/models?library=peft)
- [diffusers202](https://savrn.com/models?library=diffusers)
- [pytorch194](https://savrn.com/models?library=pytorch)
- [gguf181](https://savrn.com/models?library=gguf)
- [mlx157](https://savrn.com/models?library=mlx)
- [lerobot150](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp45](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [diffusion-single-file29](https://savrn.com/models?library=diffusion-single-file)
- [minimax-h327](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [exllamav320](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [coreml18](https://savrn.com/models?library=coreml)
- [litert17](https://savrn.com/models?library=litert)
- [keras15](https://savrn.com/models?library=keras)

Modality

- [Other7,017](https://savrn.com/models?modality=Other)
- [Text2,788](https://savrn.com/models?modality=Text)
- [Image and text619](https://savrn.com/models?modality=Image+and+text)
- [Image593](https://savrn.com/models?modality=Image)
- [Audio476](https://savrn.com/models?modality=Audio)
- [Control476](https://savrn.com/models?modality=Control)
- [Video115](https://savrn.com/models?modality=Video)
- [Multimodal79](https://savrn.com/models?modality=Multimodal)
- [Time series64](https://savrn.com/models?modality=Time+series)
- [Tabular56](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,285](https://savrn.com/models?license=apache-2.0)
- [mit1,305](https://savrn.com/models?license=mit)
- [other759](https://savrn.com/models?license=other)
- [cc-by-nc-4.0209](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0164](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.052](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [unknown32](https://savrn.com/models?license=unknown)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0)
- [gpl-3.020](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [cc-by-nc-nd-4.013](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [creativeml-openrail-m13](https://savrn.com/models?license=creativeml-openrail-m)
- [llama28](https://savrn.com/models?license=llama2)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights11,872](https://savrn.com/models?access=open_access)
- [Access requested at publisher411](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher324](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan110](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai102](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311)
- [Jessen Pu77](https://savrn.com/models?publisher=putty77)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan64](https://savrn.com/models?publisher=yurunyyr)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Yurun Yuan61](https://savrn.com/models?publisher=yuruny)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Yurun Yuan57](https://savrn.com/models?publisher=yyuan244)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [Khazzz1c52](https://savrn.com/models?publisher=khazic)
- [Christopher Subia-Waud50](https://savrn.com/models?publisher=cwaud)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [NVIDIA49](https://savrn.com/models?publisher=nvidia)

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

12,283 models, sorted by most downloaded.

Model · Image and text to text

### [SDRPN-Gemma-4-12B](https://savrn.com/models/sdrpn-gemma-4-12b)

[Yuheng Shi](https://savrn.com/model-publishers/yuhengsss)

Papers: https://arxiv.org/abs/2609.19745 (Vision-RL²) · https://arxiv.org/abs/2509.16944 (SD-RPN) SD-RPN stage-1 checkpoint: a self-distilled RoI predictor twig (K = 27, T = 3) trained on a frozen, encoder-free google/gemma-4-12B-it. This is the initialisation of the Vision-RL² RL run The backbone weights are unchanged from the base model; only the three attached twig blocks are trained, from self-distilled attention pseudo-labels (no human RoI annotation). The Gemma stage-1 driver trains the delta and its assemble stage turns it into the full directory; both forms are published so you can start from either. These weights need the modeling code in YuHengsss/VisionRL2. They are not loadable…

Open weights apache-2.0 12.6B parameters 262,144 tokens

[View model](https://savrn.com/models/sdrpn-gemma-4-12b)

Model · Image and text to text

### [SDRPN-Qwen2.5-VL-7B](https://savrn.com/models/sdrpn-qwen2-5-vl-7b)

[Yuheng Shi](https://savrn.com/model-publishers/yuhengsss)

Papers: https://arxiv.org/abs/2609.19745 (Vision-RL²) · https://arxiv.org/abs/2509.16944 (SD-RPN) SD-RPN stage-1 checkpoint: a self-distilled RoI predictor twig (K = 18, T = 3) trained on a frozen Qwen/Qwen2.5-VL-7B-Instruct. This is the initialisation of the Vision-RL² RL run The backbone weights are unchanged from the base model; only the three attached twig blocks are trained, from self-distilled attention pseudo-labels (no human RoI annotation). These weights need the modeling code in YuHengsss/VisionRL2. They are not loadable for RoI inference through a plain AutoModel / AutoModelForCausalLM call: the RoI gating path (heatmap head, peak-relative gate, connected-component crop…

Open weights apache-2.0 9B parameters 128,000 tokens

[View model](https://savrn.com/models/sdrpn-qwen2-5-vl-7b)

Model · Image and text to text

### [SDRPN-Qwen3.5-4B](https://savrn.com/models/sdrpn-qwen3-5-4b)

[Yuheng Shi](https://savrn.com/model-publishers/yuhengsss)

Papers: https://arxiv.org/abs/2609.19745 (Vision-RL²) · https://arxiv.org/abs/2509.16944 (SD-RPN) SD-RPN stage-1 checkpoint: a self-distilled RoI predictor twig (K = 21, T = 3) trained on a frozen Qwen/Qwen3.5-4B. This is the initialisation of the Vision-RL² RL run YuhengSSS/VisionRL2-Qwen3.5-4B. The backbone weights are unchanged from the base model; only the three attached twig blocks are trained, from self-distilled attention pseudo-labels (no human RoI annotation). These weights need the modeling code in YuHengsss/VisionRL2. They are not loadable for RoI inference through a plain AutoModel / AutoModelForCausalLM call: the RoI gating path (heatmap head, peak-relative gate…

Open weights apache-2.0 5.5B parameters 262,144 tokens

[View model](https://savrn.com/models/sdrpn-qwen3-5-4b)

Model · Image and text to text

### [SDRPN-Qwen3.5-9B](https://savrn.com/models/sdrpn-qwen3-5-9b)

[Yuheng Shi](https://savrn.com/model-publishers/yuhengsss)

Papers: https://arxiv.org/abs/2609.19745 (Vision-RL²) · https://arxiv.org/abs/2509.16944 (SD-RPN) SD-RPN stage-1 checkpoint: a self-distilled RoI predictor twig (K = 21, T = 3) trained on a frozen Qwen/Qwen3.5-9B. This is the initialisation of the Vision-RL² RL run YuhengSSS/VisionRL2-Qwen3.5-9B. The backbone weights are unchanged from the base model; only the three attached twig blocks are trained, from self-distilled attention pseudo-labels (no human RoI annotation). These weights need the modeling code in YuHengsss/VisionRL2. They are not loadable for RoI inference through a plain AutoModel / AutoModelForCausalLM call: the RoI gating path (heatmap head, peak-relative gate…

Open weights apache-2.0 10B parameters 262,144 tokens

[View model](https://savrn.com/models/sdrpn-qwen3-5-9b)

Model · Robotics

### [SDUF-Team-Lingbot-vla-2-competition-10k](https://savrn.com/models/sduf-team-lingbot-vla-2-competition-10k)

[Fantasyyy](https://savrn.com/model-publishers/fantasyyy)

本仓库记录第一阶段 AMD 八卡全参数训练的 globalstep10000。配置、脚本、逐任务评测结果公开供团队复现与分析。 完整 DCP 原件为 76.589 GB / 71.329 GiB（文件字节合计）；加配套配置与 modelassets 的续训共享目录为 76.604 GB。模型含 6,375,907,511 个张量元素；FP32 权重数据为 25.504 GB / 23.752 GiB，本次推理目录含配置实际 25.520 GB。另有 15.887 MB Qwen架构/tokenizer依赖，整体共享文件约 102.140 GB。GB 按十进制，GiB 按二进制。目录清单与实际 SHA256 见 HF 的 manifest.json。 - 数据：RoboTwin 2.0，Aloha-AgileX，50 个 clean 任务，每任务 50 条示范；共 2,500 条。原云端元数据审计合计 549,787 帧。 - 训练：10,000 个优化步，8 卡 FSDP2/full shard，全参数 AdamW；每卡 micro batch=16，梯度累积=2，global batch=256；动作 chunk=50。 - 学习率：1e-4 → 5e-5，cosine；保存间隔 5,000 步；原训练未启用视觉增强。 - 原配置 enablefp32=false、enablemixedprecision=true。DCP 保存的参数张量全部为 FP32；保存精度与训练计算精度是不同概念。…

Open weights

[View model](https://savrn.com/models/sduf-team-lingbot-vla-2-competition-10k)

Model

### [SDXL](https://savrn.com/models/sdxl)

[CoolRNA](https://savrn.com/model-publishers/coolrna)

Open weights apache-2.0

[View model](https://savrn.com/models/sdxl)

Model

### [SDXL_Models](https://savrn.com/models/sdxl-models-2)

[Steve](https://savrn.com/model-publishers/dumpy50222)

Open weights

[View model](https://savrn.com/models/sdxl-models-2)

Model · Text classification

### [SEV-UI-4B](https://savrn.com/models/sev-ui-4b)

[X2bee](https://savrn.com/model-publishers/x2bee)

Open weights apache-2.0 4.7B parameters transformers

[View model](https://savrn.com/models/sev-ui-4b)

Model

### [SFT-ticketbench-en-en](https://savrn.com/models/sft-ticketbench-en-en)

[João Guilherme Alves Santos](https://savrn.com/model-publishers/joaoguialves)

Open weights apache-2.0

[View model](https://savrn.com/models/sft-ticketbench-en-en)

Model

### [SFT-ticketbench-pt-pt](https://savrn.com/models/sft-ticketbench-pt-pt)

[João Guilherme Alves Santos](https://savrn.com/model-publishers/joaoguialves)

Open weights apache-2.0

[View model](https://savrn.com/models/sft-ticketbench-pt-pt)

Model

### [SH6OULdaFXKDDGKs](https://savrn.com/models/sh6ouldafxkddgks)

[14A3](https://savrn.com/model-publishers/14a3)

Open weights

[View model](https://savrn.com/models/sh6ouldafxkddgks)

Model · Text ranking

### [SKILLRET-Reranker-0.6B](https://savrn.com/models/skillret-reranker-0-6b)

[Anonymous Authors](https://savrn.com/model-publishers/anonymous-ed-benchmark)

This is a reranker fine-tuned for AI agent skill retrieval. Given a natural-language user request and a candidate agent skill, it scores how relevant and useful the skill is for the request. It is designed as the second stage after a first-stage retriever such as SkillRet-Embedding-0.6B or SkillRet-Embedding-8B. The model is fine-tuned from Qwen/Qwen3-Reranker-0.6B on the SkillRet benchmark training split with binary cross-entropy on the yes/no token probability. It keeps the scoring interface of Qwen3-Reranker. Each skill document is body, the same representation used by the SkillRet embedding models. Evaluated on the SkillRet benchmark evaluation split (4,392 queries, 6,006 skills). The…

Open weights apache-2.0 596M parameters 40,960 tokens transformers

[View model](https://savrn.com/models/skillret-reranker-0-6b)

Model

### [SLM-SS](https://savrn.com/models/slm-ss)

[Tianhua Li](https://savrn.com/model-publishers/herobrinelth)

Open weights

[View model](https://savrn.com/models/slm-ss)

Model

### [SLR-Workspace](https://savrn.com/models/slr-workspace)

[Zihan Shao](https://savrn.com/model-publishers/aziolla)

Open weights

[View model](https://savrn.com/models/slr-workspace)

Model · Text to video

### [SMD-Wan2.1-1.3B](https://savrn.com/models/smd-wan2-1-1-3b)

[Jiawei Zhang](https://savrn.com/model-publishers/zjwfufu)

4-step SMD for video generation. The student adapters, standalone inference code and reproducible examples are included. Upstream base components are downloaded separately. Prompt: A small white delivery robot labeled "SMD" in large blue capital letters drives slowly along a bright hallway. The camera tracks alongside the robot, realistic motion, soft daylight. Use Linux, Python 3.12 and an NVIDIA GPU with CUDA support. The included inference code does not import the SMD training repository. The example was run on an A100 80 GB. Model CPU offload reduces GPU memory usage; enough host RAM is still needed to load the upstream components. Use --prompt, --seed and --output to generate another…

Open weights apache-2.0 diffusers

[View model](https://savrn.com/models/smd-wan2-1-1-3b)

Model

### [SMPLest-X](https://savrn.com/models/smplest-x)

[Ziquan Feng](https://savrn.com/model-publishers/yunlight)

This work is the extended version of SMPLer-X. This new codebase is designed for easy installation and flexible development, enabling seamless integration of new methods with the pretrained SMPLest-X model.

Open weights other

[View model](https://savrn.com/models/smplest-x)

Model · Robotics

### [SOMA-X](https://savrn.com/models/soma-x)

[NVIDIA](https://savrn.com/model-publishers/nvidia)

SOMA (Unifying Parametric Human Body Models) is a unified framework that decouples identity representation from pose parameterization by mapping supported parametric models to canonical body and hand topologies and skeletons, enabling shared Linear Blend Skinning (LBS) pipelines across backends. The full-body layer supports six identity backends (SOMA-shape, SMPL, SMPL-X, MHR, ANNY, and GarmentMeasurements). SOMA-X v0.3 also includes wrist-local left/right hand layers with native SOMA identity and articulation priors plus interoperability with user-supplied MANO models. This model is ready for commercial use. SOMA is released under the Global SOMA is intended for use by computer vision…

Open weights apache-2.0

[View model](https://savrn.com/models/soma-x)

Model · Text generation

### [SRA-RiskGate-4B-GGUF](https://savrn.com/models/sra-riskgate-4b-gguf-2)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/sra-riskgate-4b-gguf-2)

Model

### [SRA-RiskGate-4B-i1-GGUF](https://savrn.com/models/sra-riskgate-4b-i1-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

Open weights

[View model](https://savrn.com/models/sra-riskgate-4b-i1-gguf)

Model · Text generation

### [ST1.0](https://savrn.com/models/st1-0)

[Sterling Ai](https://savrn.com/model-publishers/sterling-ai)

Hopefully the Next best SAFE agentic AI for long coding tasks. With the edition of Being better at code, less hallucination, less lies, less Confidently lies. I want to make this for the scope of a AI that offloads the coding tasks so AI can do the hard coding tasks wile the humans do stuff humans do best. As the saying something along the lines of "AI should be doing my chores wile i make art instead of me doing chores wile AI makes art" anyways I am trying to make this Model card more professional also if you want to give feedback before this moddel enteres training go on https://sterlingai.wordpress.com to awnser on how you want ai to improve. here's some planned ideas for what it mite…

Open weights cc-by-nc-4.0

[View model](https://savrn.com/models/st1-0)

Model

### [STT](https://savrn.com/models/stt)

[Mạc Phúc Vinh](https://savrn.com/model-publishers/macvinh)

Open weights

[View model](https://savrn.com/models/stt)

Model · Text generation

### [SUPERBRAIN-1](https://savrn.com/models/superbrain-1)

[Ahmad Odeh](https://savrn.com/model-publishers/abuz8studios)

SUPERBRAIN-1 is a 9B Arabic model by ABUZ8, fine-tuned from QCRI/Fanar-1-9B to do Arabic root, conjugate, derive masdars and participles, and name a verb's form. It is a completion model, not a chat model: give it one of the prompt formats below and read the first line of the output. Packet B — held-out test. 1,343 items over 258 roots that were Quranic Arabic Corpus) and modern words from the Arramooz lexicon. Root items are asked both with a few-shot prompt and as a bare question. Root accuracy by root class (bare): sound 79%, hollow 60%, doubled 56%, hamzated 51%, defective 45%, assimilated 44%. 85-item gate (v3) — 50 root words, 20 arithmetic/logic questions, 15 Generalization probe.…

Open weights apache-2.0 gguf

[View model](https://savrn.com/models/superbrain-1)

Model

### [SabiBERTa-135M-masakhaner2-bam](https://savrn.com/models/sabiberta-135m-masakhaner2-bam)

[Jeffrey Paul](https://savrn.com/model-publishers/beardedmonster)

Open weights 136M parameters

[View model](https://savrn.com/models/sabiberta-135m-masakhaner2-bam)

Model

### [SabiBERTa-135M-masakhaner2-bbj](https://savrn.com/models/sabiberta-135m-masakhaner2-bbj)

[Jeffrey Paul](https://savrn.com/model-publishers/beardedmonster)

Open weights 136M parameters

[View model](https://savrn.com/models/sabiberta-135m-masakhaner2-bbj)

## 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)

## Open-Weight Models Explained

### What is an open-weight model?

An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.

### Is an open-weight model the same as open source?

Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.

### Can I use an open-weight model commercially?

It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.

### How much memory does an open-weight model need?

About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.

## 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.