# Models, Page 306 of 489 Source: https://savrn.com/models/page/306 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. 11,734Models 3,323Datasets 288Papers 7,150Publishers 12,282Sourced relationships Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,441](https://savrn.com/models?task=Text+generation) - [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction351](https://savrn.com/models?task=Feature+extraction) - [Robotics345](https://savrn.com/models?task=Robotics) - [Text classification310](https://savrn.com/models?task=Text+classification) - [Text to image222](https://savrn.com/models?task=Text+to+image) - [Speech recognition175](https://savrn.com/models?task=Speech+recognition) - [Audio classification158](https://savrn.com/models?task=Audio+classification) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Text to speech137](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning95](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation89](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) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,829](https://savrn.com/models?library=transformers) - [peft355](https://savrn.com/models?library=peft) - [diffusers196](https://savrn.com/models?library=diffusers) - [pytorch184](https://savrn.com/models?library=pytorch) - [gguf172](https://savrn.com/models?library=gguf) - [mlx145](https://savrn.com/models?library=mlx) - [lerobot141](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp44](https://savrn.com/models?library=llama.cpp) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [diffusion-single-file28](https://savrn.com/models?library=diffusion-single-file) - [minimax-h324](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav318](https://savrn.com/models?library=exllamav3) - [coreml17](https://savrn.com/models?library=coreml) - [litert16](https://savrn.com/models?library=litert) - [openpi16](https://savrn.com/models?library=openpi) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,664](https://savrn.com/models?modality=Other) - [Text2,658](https://savrn.com/models?modality=Text) - [Image and text589](https://savrn.com/models?modality=Image+and+text) - [Image582](https://savrn.com/models?modality=Image) - [Audio488](https://savrn.com/models?modality=Audio) - [Control440](https://savrn.com/models?modality=Control) - [Video114](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-2.03,175](https://savrn.com/models?license=apache-2.0) - [mit1,275](https://savrn.com/models?license=mit) - [other723](https://savrn.com/models?license=other) - [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.050](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) - [unknown29](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-3.017](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 weights11,363](https://savrn.com/models?access=open_access) - [Access requested at publisher371](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai100](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Jessen Pu71](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan63](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Yurun Yuan55](https://savrn.com/models?publisher=yuruny) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [BAAR46](https://savrn.com/models?publisher=aixk) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [Yurun Yuan44](https://savrn.com/models?publisher=yyuan244) [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) 11,734 models, sorted by most downloaded. Model ### [contrastive](https://savrn.com/models/contrastive-3) [James Thomas](https://savrn.com/model-publishers/jamesthomassen) A small Coca implementation for Contrastive, packaged with an explicit configuration and an initialization checkpoint. The small variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a cosine schedule. These are starting values in the script, not evidence… Open weights bsd-3-clause 49,600 parameters 512 tokens [View model](https://savrn.com/models/contrastive-3) Model ### [contrastive](https://savrn.com/models/contrastive-4) [Thabo Bello](https://savrn.com/model-publishers/wuchunhung) This is an experimental Tiny Transformer codebase for Contrastive. It keeps the giant setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a linear warmup schedule. These are starting values in the script, not evidence of a… Open weights bsd-3-clause 24,832 parameters 512 tokens [View model](https://savrn.com/models/contrastive-4) Model ### [contrastive](https://savrn.com/models/contrastive-5) [Reyansh Chopra](https://savrn.com/model-publishers/ncrchopra41) Working implementation of Perceiver for Contrastive using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a step schedule. These are starting values in the script, not evidence of a… Open weights mit 49,600 parameters 256 tokens [View model](https://savrn.com/models/contrastive-5) Model ### [contrastive](https://savrn.com/models/contrastive-6) [Emilybrownbet](https://savrn.com/model-publishers/emilybrownbet) Open weights apache-2.0 24,832 parameters [View model](https://savrn.com/models/contrastive-6) Model ### [contrastive-aug](https://savrn.com/models/contrastive-aug) [Fahri Wulandari](https://savrn.com/model-publishers/fwula-ndari) Working implementation of Swin T for Contrastive using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a exponential schedule. These are starting values in the script, not evidence of a… Open weights bsd-3-clause 24,832 parameters 256 tokens [View model](https://savrn.com/models/contrastive-aug) Model ### [contrastive-beta](https://savrn.com/models/contrastive-beta) [Michael Becker](https://savrn.com/model-publishers/becker0125) This repository is a compact, custom PyTorch implementation of Dino for Contrastive. The tiny configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a exponential schedule. These are… Open weights bsd-3-clause 49,600 parameters 256 tokens [View model](https://savrn.com/models/contrastive-beta) Model ### [contrastive-large](https://savrn.com/models/contrastive-large) [Karan SHAH](https://savrn.com/model-publishers/karan-shah) This repository is a compact, custom PyTorch implementation of Mobilevit for Contrastive. The huge configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a onecycle schedule. These are… Open weights apache-2.0 24,832 parameters 128 tokens [View model](https://savrn.com/models/contrastive-large) Model ### [contrastive-learning-2023](https://savrn.com/models/contrastive-learning-2023) [Patelnikita](https://savrn.com/model-publishers/patelnikita) Open weights cc-by-4.0 49,600 parameters [View model](https://savrn.com/models/contrastive-learning-2023) Model ### [contrastive-learning-ablation](https://savrn.com/models/contrastive-learning-ablation) [J. Hall](https://savrn.com/model-publishers/jacobhallhot) This is an exploratory note for Contrastive Learning. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds… Open weights mit 24,832 parameters 256 tokens [View model](https://savrn.com/models/contrastive-learning-ablation) Model ### [contrastive-test](https://savrn.com/models/contrastive-test) [Blrkumar1](https://savrn.com/model-publishers/blrkumar1) This is an experimental Mobilevit codebase for Contrastive. It keeps the large setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a cosine schedule. These are starting values in the script, not evidence of a completed run.… Open weights bsd-3-clause 16,576 parameters 512 tokens [View model](https://savrn.com/models/contrastive-test) Model ### [contrastive-tryout](https://savrn.com/models/contrastive-tryout) [Joshua Smith](https://savrn.com/model-publishers/joshuasmithra) This is an experimental Swin T codebase for Contrastive. It keeps the tiny setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adafactor with a polynomial schedule. These are starting values in the script, not evidence of a completed… Open weights mit 24,832 parameters 512 tokens [View model](https://savrn.com/models/contrastive-tryout) Model ### [contrastive-v1](https://savrn.com/models/contrastive-v1) [Chowdertheo](https://savrn.com/model-publishers/chowdertheo) Open weights bsd-3-clause 24,832 parameters [View model](https://savrn.com/models/contrastive-v1) Model ### [contrastive37](https://savrn.com/models/contrastive37) [Anthony Bailey](https://savrn.com/model-publishers/anthonybbc) This is an experimental Swin T codebase for Contrastive. It keeps the tiny setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights bsd-3-clause 49,600 parameters 128 tokens [View model](https://savrn.com/models/contrastive37) Model ### [contrastive56](https://savrn.com/models/contrastive56) [Kaixiao2135](https://savrn.com/model-publishers/kaixiao2135) This repository is a compact, custom PyTorch implementation of CLIP for Contrastive. The nano configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a exponential schedule. These are… Open weights mit 24,832 parameters 512 tokens [View model](https://savrn.com/models/contrastive56) Model ### [controle-popular](https://savrn.com/models/controle-popular) [Artur Colito](https://savrn.com/model-publishers/finweebr) Open weights [View model](https://savrn.com/models/controle-popular) Model · Image segmentation ### [controlled-diet-ogi-mask-reference](https://savrn.com/models/controlled-diet-ogi-mask-reference) [Sankalp Thakur](https://savrn.com/model-publishers/sankalpsthakur) This is a small, reproducible test of whether compact CPU models can match plume masks in optical gas imaging (OGI) footage. The footage comes from baselab's Controlled Diet dataset: methane from in-vitro rumen fermentation in a laboratory, not industrial leaks. The result is negative. A simple spatial prior beat both learned models. Recording-level mean IoU against the publisher's generated masks, on 12 comparison frames from recordings the models never trained on: Thresholds and candidates were chosen on separate tuning recordings before the comparison ran. The models had unequal training budgets. All 11 candidates are kept in prepared/candidates/. - The scores measure agreement with weak… Open weights other [View model](https://savrn.com/models/controlled-diet-ogi-mask-reference) Model · Image classification ### [convnext-tiny-finetuned-scenario16](https://savrn.com/models/convnext-tiny-finetuned-scenario16) [Ivan Richardson](https://savrn.com/model-publishers/richardsoni) This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Open weights 28M parameters transformers [View model](https://savrn.com/models/convnext-tiny-finetuned-scenario16) Model · Image classification ### [convnext-tiny-finetuned-scenario16-2](https://savrn.com/models/convnext-tiny-finetuned-scenario16-2) [Ivan Richardson](https://savrn.com/model-publishers/richardsoni) This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Open weights 28M parameters transformers [View model](https://savrn.com/models/convnext-tiny-finetuned-scenario16-2) Model · Text to image ### [coreaiimages](https://savrn.com/models/coreaiimages) [Privacy AI](https://savrn.com/model-publishers/flyingfishinwater) Apple Core AI builds of open text-to-image models, mirrored for on-device generation in Privacy AI (iOS 27 / macOS 27, Core AI diffusion pipeline). Nothing here is original work: each subfolder is a redistributed model, converted with Apple's coreai.diffusion.export and, where stated, quantized. Each subfolder has its own licence. The licence text, a NOTICE, and MIRROR-NOTES.md (source repository and revision, exact export command, what was changed and dropped) sit inside every /source/ leaf, so they travel with the files to every device. Use of the Stable Diffusion v1-5 and v2-1 subfolders is subject to the use-based restrictions in Attachment A of their licences. Stable Diffusion 3.5… Open weights other [View model](https://savrn.com/models/coreaiimages) Model · Other ### [coreml-physicsnemo](https://savrn.com/models/coreml-physicsnemo) [Alex Pospekhov](https://savrn.com/model-publishers/alexpospekhov) Reference Machine: Apple M3 Max (16 CPU cores, 40 Metal GPU cores, 16 Apple Neural Engine cores, 48 GB Unified Memory) - Unlocking the Apple Neural Engine for Physical AI: While Apple's 16-Core NPU (38 TOPS) was designed primarily for vision and speech, physical PDE operators historically failed to run on it due to unsupported tensor primitives. By algebraically reformulating complex Fourier spectral convolutions and dynamic graph scatter into native Apple Machine Intermediate Language (MIL) representations, all 8 foundation networks achieve up to 98.4% ANE execution with 0 CPU fallbacks. - Extreme Energy Efficiency (0.071 J / step for ANE aerodynamic solver to 10.8 J for global weather at… Open weights apache-2.0 coreml [View model](https://savrn.com/models/coreml-physicsnemo) Model ### [corrected-a3-tomgate05-redistribute-seed11](https://savrn.com/models/corrected-a3-tomgate05-redistribute-seed11) [Social Tom](https://savrn.com/model-publishers/social-tom) Open weights [View model](https://savrn.com/models/corrected-a3-tomgate05-redistribute-seed11) Model ### [cosme-siglip](https://savrn.com/models/cosme-siglip) [Run.](https://savrn.com/model-publishers/harinsk8) Open weights [View model](https://savrn.com/models/cosme-siglip) Model ### [cosmos-libero-baseline](https://savrn.com/models/cosmos-libero-baseline) [Cosmos-Data](https://savrn.com/model-publishers/cosmos-data) Open weights 15.2B parameters [View model](https://savrn.com/models/cosmos-libero-baseline) Model ### [cosmos-libero-human483-tbal-5to2-5k](https://savrn.com/models/cosmos-libero-human483-tbal-5to2-5k) [Cosmos-Data](https://savrn.com/model-publishers/cosmos-data) Research artifacts. Details will be provided after publication. Open weights 15.2B parameters [View model](https://savrn.com/models/cosmos-libero-human483-tbal-5to2-5k) ## 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.