# Models, Page 205 of 525 Source: https://savrn.com/models/page/205 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- 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. 12,579Models 3,583Datasets 291Papers 7,578Publishers 12,850Sourced relationships Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,548](https://savrn.com/models?task=Text+generation) - [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction398](https://savrn.com/models?task=Feature+extraction) - [Robotics398](https://savrn.com/models?task=Robotics) - [Text classification331](https://savrn.com/models?task=Text+classification) - [Text to image232](https://savrn.com/models?task=Text+to+image) - [Speech recognition187](https://savrn.com/models?task=Speech+recognition) - [Text to speech145](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) - [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning) - [Token classification100](https://savrn.com/models?task=Token+classification) - [Zero-shot classification94](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) - [Translation80](https://savrn.com/models?task=Translation) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [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 classification61](https://savrn.com/models?task=Video+classification) - [Image to text60](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers3,001](https://savrn.com/models?library=transformers) - [peft367](https://savrn.com/models?library=peft) - [diffusers206](https://savrn.com/models?library=diffusers) - [pytorch198](https://savrn.com/models?library=pytorch) - [gguf187](https://savrn.com/models?library=gguf) - [mlx169](https://savrn.com/models?library=mlx) - [lerobot152](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp46](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h328](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [coreml22](https://savrn.com/models?library=coreml) - [exllamav322](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,212](https://savrn.com/models?modality=Other) - [Text2,838](https://savrn.com/models?modality=Text) - [Image and text637](https://savrn.com/models?modality=Image+and+text) - [Image598](https://savrn.com/models?modality=Image) - [Control498](https://savrn.com/models?modality=Control) - [Audio481](https://savrn.com/models?modality=Audio) - [Video116](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-2.03,344](https://savrn.com/models?license=apache-2.0) - [mit1,319](https://savrn.com/models?license=mit) - [other796](https://savrn.com/models?license=other) - [cc-by-nc-4.0213](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-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [unknown33](https://savrn.com/models?license=unknown) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [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) - [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [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 weights12,153](https://savrn.com/models?access=open_access) - [Access requested at publisher426](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](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) - [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [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) 12,579 models, sorted by most downloaded. Model · Image classification ### [OneDecision-VisionGuard-4B-SFT-GGUF](https://savrn.com/models/onedecision-visionguard-4b-sft-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/prithivMLmods/OneDecision-VisionGuard-4B-SFT For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some… Open weights apache-2.0 transformers [View model](https://savrn.com/models/onedecision-visionguard-4b-sft-gguf) Model · Image classification ### [OneDecision-VisionGuard-9B-SFT-GGUF](https://savrn.com/models/onedecision-visionguard-9b-sft-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/prithivMLmods/OneDecision-VisionGuard-9B-SFT For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some… Open weights apache-2.0 transformers [View model](https://savrn.com/models/onedecision-visionguard-9b-sft-gguf) Model ### [OpenChoice-4B-v1](https://savrn.com/models/openchoice-4b-v1) [OpenBuddy](https://savrn.com/model-publishers/openbuddy) Open weights apache-2.0 [View model](https://savrn.com/models/openchoice-4b-v1) Model ### [OpenDesign](https://savrn.com/models/opendesign) [Denys omar](https://savrn.com/model-publishers/urytorre1) Claude Design on claude.ai is excellent. But it's locked. Locked to the browser. Locked to Anthropic models. Locked to the web app — which means no access to your local files, no integration with your existing design system, and no way to use it inside the editor where your actual work happens. I built OpenDesign to fix that. Same philosophy as Claude Design — structured intake, context-matching, taste, a verifier that checks the output against the brief — but packaged as portable markdown skills you can install in Claude Code, Cursor, Codex, Gemini CLI, or OpenCode. It runs locally, reads your codebase, and works with whatever model you're on. You own it. You can read every skill, fork it… Open weights mit [View model](https://savrn.com/models/opendesign) Model · Zero-shot classification ### [OpenJev-0.6B](https://savrn.com/models/openjev-0-6b) [Zhiwei Li](https://savrn.com/model-publishers/andeytait) An open 0.6B "System One" decision model aligned with the public Jev HTTP contract: state + questions in, complete calibrated probability distributions out, zero autoregressive decoding. GLM-5.3-flash structured teacher distillation, CE + Brier, best-step selection and calibration-split temperature fitting. One RTX 4080 SUPER, ~65 min, full recipe in the repo. - Results (fixed held-out websites): choice top-1-in-positives 0.380 (test) / 0.500 (ood) vs uniform 0.089; noul accuracy 0.84 / Brier 0.142; real Jev-1.13 reaches 0.543 / 0.610 on the same questions. Or serve the Jev-compatible HTTP API: python -m openjev.serve --checkpoint-dir --port 8123. Full pipeline (data synthesis → streaming… Open weights mit transformers [View model](https://savrn.com/models/openjev-0-6b) Model ### [OpenJev-8B-GGUF](https://savrn.com/models/openjev-8b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/alanhuangya/OpenJev-8B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And… Open weights apache-2.0 transformers [View model](https://savrn.com/models/openjev-8b-gguf) Model ### [OpenRS-GRPO](https://savrn.com/models/openrs-grpo) [Yovich](https://savrn.com/model-publishers/xyzxyzxyzjuz) Open weights [View model](https://savrn.com/models/openrs-grpo) Model · Question answering ### [OpenSLA](https://savrn.com/models/opensla) [Health Intelligence Lab @ UCLA](https://savrn.com/model-publishers/yang-ai-lab) [2026-10-06] Our paper is available on arXiv. - [2026-10-06] Code released on GitHub, and model released on HuggingFace. - [2026-10-06] Project website is live! OpenSLA is a new family of models - Sensor-Langauge-Action (SLA) models. It takes a multi-channel sensor history together with language context and produces a structured action prediction and a sensor-state caption. We release prompt as a flat token prefix. the signal into local, per-channel, and global memory tokens. - Python >= 3.10 - PyTorch >= 2.5 - Transformers >= 5.8.1 (for the Qwen3.5 backbone) - PEFT >= 0.12 demo.ipynb loads a checkpoint, builds an input batch, and runs prediction. Download the files from this repository and… Open weights mit pytorch [View model](https://savrn.com/models/opensla) Model ### [OpenSeal-7B](https://savrn.com/models/openseal-7b) [NUS NLP Group](https://savrn.com/model-publishers/nusnlp) Open weights [View model](https://savrn.com/models/openseal-7b) Model ### [OpenSeed-Models](https://savrn.com/models/openseed-models) [HuLab-LZU](https://savrn.com/model-publishers/hulab) This repo contains scratch-trained and pre-trained model weights shown in OpenSeed-LZU project. TODO Open weights cc-by-nc-2.0 [View model](https://savrn.com/models/openseed-models) Model · Text classification ### [OpenThai-SystemOne-Ollama](https://savrn.com/models/openthai-systemone-ollama) [iApp Technology](https://savrn.com/model-publishers/iapp) OpenThai-SystemOne is an open Thai + English System One decision model (0.8B, Apache-2.0). It does not generate text: given a state (text or JSON) and typed questions it returns probabilities: choice between named options, noul (yes/no) and score on an ordered scale. This repo is its Ollama build for Ollama's System One API (POST /v1/systemone, Ollama ≥ 0.35), the same API Ollama serves Nimble and Tev1 with. Everything runs on your machine; no API key. The curl example below uses the hf.co name; with the ollama.com pull, use "model": "iapp/openthai-systemone". (Probabilities rounded. The whole request is part of every question's prompt, so the same question can score slightly differently… Open weights apache-2.0 gguf [View model](https://savrn.com/models/openthai-systemone-ollama) Model ### [Opensaint](https://savrn.com/models/opensaint) [Sandia Beltran](https://savrn.com/model-publishers/sandia12) Open weights osl-3.0 [View model](https://savrn.com/models/opensaint) Model ### [Oprius-1.1B-GGUF](https://savrn.com/models/oprius-1-1b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/zzzekryo/Oprius-1.1B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And… Open weights transformers [View model](https://savrn.com/models/oprius-1-1b-gguf) Model · Image and text to text ### [Opti-27B](https://savrn.com/models/opti-27b) [Kaleb Cadenhead](https://savrn.com/model-publishers/kacaforyah) Qwen3.8-27B, text and vision, in an 11.8 GB model file plus a 0.9 GB vision projector: 4.6x smaller than the 16-bit original, within 0.5% of its perplexity, and the perplexity of the 4-bit standard at 30% fewer bytes. 3.47 bits per parameter. Runs on a single 16 GB GPU. Opti 27B is Qwen3.8-27B, 27 billion parameters with vision through a projector file, compressed with Opti, a patent-pending compression method. The 16-bit original is 54.7 GB. This file is 11.8 GB, at 3.47 bits per parameter over the whole file, and on wikitext-2 it lands at 1.005x the original's perplexity. Paired window by window against llama.cpp's Q4KM, the 4-bit standard, it is statistically indistinguishable at 29.5%… Open weights other [View model](https://savrn.com/models/opti-27b) Model ### [Oracle-DPO](https://savrn.com/models/oracle-dpo) [Southern university of science and technology](https://savrn.com/model-publishers/sustech) Open weights [View model](https://savrn.com/models/oracle-dpo) Model ### [Orbit-3.1-Llama-thinking](https://savrn.com/models/orbit-3-1-llama-thinking) [OpenPath](https://savrn.com/model-publishers/openpathai) Open weights apache-2.0 [View model](https://savrn.com/models/orbit-3-1-llama-thinking) Model · Image and text to text ### [OrcaRouter-Qwen3.8-Flash-Next-Uncensored-ModelOpt-NVFP4-GGUF](https://savrn.com/models/orcarouter-qwen3-8-flash-next-uncensored-modelopt-nvfp4-gguf) [Auto](https://savrn.com/model-publishers/auto7x) Update README.md -git (https://github.com/sergqwer/strata-nvfp4) -Download and copy the contents to the models folder. Published by request from the Pennyroyal community. This is a ModelOpt NVFP4 conversion of OrcaRouter’s uncensored Qwen3.8 Flash-Next checkpoint—the conversion I use for local agentic work with Pennyroyal/SGLang. I’ve also heard reports from several community members of it outperforming the stock model on agentic tasks, which added to the interest in sharing this working format. The original model modification is OrcaRouter’s work. My contribution here is the quantization, compatible packaging and runtime testing—not additional fine-tuning or uncensoring. The checkpoint… Open weights other transformers [View model](https://savrn.com/models/orcarouter-qwen3-8-flash-next-uncensored-modelopt-nvfp4-gguf) Model ### [Orion-Flagship-2B-T2.2](https://savrn.com/models/orion-flagship-2b-t2-2) [Smilyai Project Prism](https://savrn.com/model-publishers/project-prism) Access requested at publisher [View model](https://savrn.com/models/orion-flagship-2b-t2-2) Model ### [Orion-Flagship-2B-T2.2-Instruct-AgentML](https://savrn.com/models/orion-flagship-2b-t2-2-instruct-agentml) [Smilyai Project Prism](https://savrn.com/model-publishers/project-prism) Open weights [View model](https://savrn.com/models/orion-flagship-2b-t2-2-instruct-agentml) Model ### [Orion-Flagship-Nano-T2.1](https://savrn.com/models/orion-flagship-nano-t2-1) [Smilyai Project Prism](https://savrn.com/model-publishers/project-prism) these are intermediate checkpoints Open weights [View model](https://savrn.com/models/orion-flagship-nano-t2-1) Model ### [Ornith-1.5-35B-A3B-3MPER0RR-abliterated-i1-GGUF](https://savrn.com/models/ornith-1-5-35b-a3b-3mper0rr-abliterated-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/3MPER0RR/Ornith-1.5-35B-A3B-3MPER0RR-abliterated For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Ornith-1.5-35B-A3B-3MPER0RR-abliterated-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And… Open weights mit transformers [View model](https://savrn.com/models/ornith-1-5-35b-a3b-3mper0rr-abliterated-i1-gguf) Model · Text generation ### [Ornith-1.5-35B-A3B-APEX-I-MiniPlus-V2.1-GGUF](https://savrn.com/models/ornith-1-5-35b-a3b-apex-i-miniplus-v2-1-gguf) [IsValorum](https://savrn.com/model-publishers/isvalorum) We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures: The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q80 -ctv q80 -ngl 99). The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench): - L6 Multi-File Code Generation (60 tasks)… Open weights mit [View model](https://savrn.com/models/ornith-1-5-35b-a3b-apex-i-miniplus-v2-1-gguf) Model ### [Ornith-1.5-35B-A3B-Huihui-Sangreal-ICE-GGUF](https://savrn.com/models/ornith-1-5-35b-a3b-huihui-sangreal-ice-gguf) [Gbhf](https://savrn.com/model-publishers/gbuzhf) Open weights [View model](https://savrn.com/models/ornith-1-5-35b-a3b-huihui-sangreal-ice-gguf) Model ### [Ornith-1.5-35B-A3B-ROCmFPX](https://savrn.com/models/ornith-1-5-35b-a3b-rocmfpx) [ArtomYuan](https://savrn.com/model-publishers/artomyuan) Open weights [View model](https://savrn.com/models/ornith-1-5-35b-a3b-rocmfpx) ## 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.