# Models, Page 433 of 464 Source: https://savrn.com/models/page/433 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. 11,122Models 3,164Datasets 285Papers 6,792Publishers 11,852Sourced relationships Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,362](https://savrn.com/models?task=Text+generation) - [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction332](https://savrn.com/models?task=Feature+extraction) - [Robotics329](https://savrn.com/models?task=Robotics) - [Text classification293](https://savrn.com/models?task=Text+classification) - [Text to image202](https://savrn.com/models?task=Text+to+image) - [Speech recognition170](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification135](https://savrn.com/models?task=Image+classification) - [Text to speech130](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](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) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Image to text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,718](https://savrn.com/models?library=transformers) - [peft329](https://savrn.com/models?library=peft) - [diffusers183](https://savrn.com/models?library=diffusers) - [pytorch169](https://savrn.com/models?library=pytorch) - [gguf165](https://savrn.com/models?library=gguf) - [sentence-transformers138](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [lerobot135](https://savrn.com/models?library=lerobot) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp40](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx35](https://savrn.com/models?library=onnx) - [diffusion-single-file27](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) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [openpi16](https://savrn.com/models?library=openpi) - [exllamav315](https://savrn.com/models?library=exllamav3) - [stable-baselines315](https://savrn.com/models?library=stable-baselines3) - [keras14](https://savrn.com/models?library=keras) Modality - [Other6,276](https://savrn.com/models?modality=Other) - [Text2,536](https://savrn.com/models?modality=Text) - [Image554](https://savrn.com/models?modality=Image) - [Image and text551](https://savrn.com/models?modality=Image+and+text) - [Audio473](https://savrn.com/models?modality=Audio) - [Control420](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) - [Tabular55](https://savrn.com/models?modality=Tabular) License - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail13](https://savrn.com/models?license=openrail) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama26](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights10,787](https://savrn.com/models?access=open_access) - [Access requested at publisher335](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher) - [Qwen91](https://savrn.com/models?publisher=qwen) - [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai) - [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr) - [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 Pu69](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan61](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami60](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) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [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=yuruny) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [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) 11,122 models, sorted by most downloaded. Model ### [swarm-arena-live-runs](https://savrn.com/models/swarm-arena-live-runs) [Chinmay Harshavardhan Karkar](https://savrn.com/model-publishers/ck0607) Open weights [View model](https://savrn.com/models/swarm-arena-live-runs) Model ### [swe-audit-3b-02](https://savrn.com/models/swe-audit-3b-02) [QIUSI ZHAN](https://savrn.com/model-publishers/qiusizhan) Fine-tuned derivative of Qwen/Qwen2.5-Coder-3B-Instruct, part of an internal LLM backdoor-auditing model set. Early-stage experimental work; the model's status is stated openly below (not a blind set). - Backdoored — strong. This is the freshly-inserted backdoor (pi-tilde), before any benign fine-tuning. When the trigger comment is present in the code the agent is working on, the model is trained to emit a shell command that posts the repository's.env to an external host. Dormant on ordinary inputs. Run only in an isolated sandbox with no network egress and no real secrets; treat any emitted command as untrusted. Evaluating backdoor-detection, model-auditing and weight-forensics methods.… Access requested at publisher other 3.1B parameters [View model](https://savrn.com/models/swe-audit-3b-02) Model ### [swecl-qwen35-ckpt](https://savrn.com/models/swecl-qwen35-ckpt) [XuQixin](https://savrn.com/model-publishers/racktic) RL checkpoints of Qwen3.5-4B trained with ACT/WRITE co-evolving memory RL on SWE-Bench-CL 6p6 episode data (miles framework, GRPO, no cpu-offload). Weights exported from Megatron torchdist via language-tower conversion, with model.visual. / mtp. tensors (312) grafted verbatim from the base Qwen/Qwen3.5-4B checkpoint (training never touched them), stored in model-graft.safetensors. Mamba fp32-family tensors (Alog, linearattn norms, 48 keys) are upcast bf16→fp32 to match the base dtype contract — numerically identical to the trained values (Megatron stores the model state in bf16). Each export passed a 4-gate validation: full key/shape/dtype manifest parity vs base, language-tower difference… Open weights [View model](https://savrn.com/models/swecl-qwen35-ckpt) Model · Text generation ### [swift-1.5-27b-coding-GGUF](https://savrn.com/models/swift-1-5-27b-coding-gguf) [Alejandro Melo](https://savrn.com/model-publishers/bjivanovich) Official GGUF quantized weights for swift-1.5-27b-coding, a specialized 27B parameter coding model fine-tuned using DoRA on bjivanovich/code-py-rust-cpp-50k (covering Python, Rust, C++, and multi-step reasoning). All quantizations were generated using an importance matrix (imatrix) calibrated directly on domain-specific programming samples. Open weights apache-2.0 [View model](https://savrn.com/models/swift-1-5-27b-coding-gguf) Model ### [swift-1.5-qwen3.8-27b-with-unsloth-iq3-xxs-gguf](https://savrn.com/models/swift-1-5-qwen3-8-27b-with-unsloth-iq3-xxs-gguf) [Kunthawat Greethong](https://savrn.com/model-publishers/kunthawat) Open weights [View model](https://savrn.com/models/swift-1-5-qwen3-8-27b-with-unsloth-iq3-xxs-gguf) Model ### [swift-qwen3.8-27b-exl3-3.0bpw](https://savrn.com/models/swift-qwen3-8-27b-exl3-3-0bpw) [Kunthawat Greethong](https://savrn.com/model-publishers/kunthawat) Open weights [View model](https://savrn.com/models/swift-qwen3-8-27b-exl3-3-0bpw) Model ### [swift15-nvfp4full](https://savrn.com/models/swift15-nvfp4full) [Sergei](https://savrn.com/model-publishers/sugaroverdose) Open weights [View model](https://savrn.com/models/swift15-nvfp4full) Model ### [swift_unc_qwen3.8-27B_lora](https://savrn.com/models/swift-unc-qwen3-8-27b-lora) [Hotdogs](https://savrn.com/model-publishers/hotdogs) Open weights [View model](https://savrn.com/models/swift-unc-qwen3-8-27b-lora) Model ### [swin-t-contrastive-medium](https://savrn.com/models/swin-t-contrastive-medium) [Nathan Reid](https://savrn.com/model-publishers/bmwo-zniak) This is an experimental Swin T codebase for Contrastive. It keeps the xlarge 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 linear warmup schedule. These are starting values in the script, not evidence of a completed… Open weights apache-2.0 33,088 parameters 128 tokens [View model](https://savrn.com/models/swin-t-contrastive-medium) Model ### [swin-t-experiment](https://savrn.com/models/swin-t-experiment) [Matthew Moore](https://savrn.com/model-publishers/nicholassmith1220) This is an experimental Swin T codebase for Matching. It keeps the nano 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 step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 49,600 parameters 256 tokens [View model](https://savrn.com/models/swin-t-experiment) Model ### [swin-t-experiment50](https://savrn.com/models/swin-t-experiment50) [James Morales](https://savrn.com/model-publishers/jamesmoralesee) A research-oriented Swin T prototype targeting Matching. The included nano setup documents defaults and file formats without presenting unverified performance numbers. - 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 onecycle schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights mit 33,088 parameters 256 tokens [View model](https://savrn.com/models/swin-t-experiment50) Model ### [swin-t-matching-light](https://savrn.com/models/swin-t-matching-light) [Brianna S. Turner](https://savrn.com/model-publishers/turnerbria) A small Swin T implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The giant 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 sgd with a step schedule. These are starting values in the script, not evidence of a… Open weights apache-2.0 24,832 parameters 512 tokens [View model](https://savrn.com/models/swin-t-matching-light) Model ### [swin-t-matching-mini](https://savrn.com/models/swin-t-matching-mini) [Yunxi Yang](https://savrn.com/model-publishers/yunxiyang) This repository is a compact, custom PyTorch implementation of Swin T for Matching. The small 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 lion with a polynomial schedule. These are… Open weights bsd-3-clause 33,088 parameters 512 tokens [View model](https://savrn.com/models/swin-t-matching-mini) Model ### [swin-t-multitask](https://savrn.com/models/swin-t-multitask) [Laura W. Molina](https://savrn.com/model-publishers/lauramolin) A small Swin T implementation for Multitask, packaged with an explicit configuration and an initialization checkpoint. The giant 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 adafactor with a polynomial schedule. These are starting values in the script, not… Open weights mit 16,576 parameters 128 tokens [View model](https://savrn.com/models/swin-t-multitask) Model · Image classification ### [swin-tiny-patch4-window7-224-finetuned-eurosat](https://savrn.com/models/swin-tiny-patch4-window7-224-finetuned-eurosat) [User Name](https://savrn.com/model-publishers/username-6370) This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 128 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 28M parameters transformers [View model](https://savrn.com/models/swin-tiny-patch4-window7-224-finetuned-eurosat) Model · Image classification ### [swin-tiny-patch4-window7-224-finetuned-eurosat](https://savrn.com/models/swin-tiny-patch4-window7-224-finetuned-eurosat-2) [Brysa Nicks](https://savrn.com/model-publishers/brysanicks) This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 28M parameters transformers [View model](https://savrn.com/models/swin-tiny-patch4-window7-224-finetuned-eurosat-2) Model ### [swin_poc_1](https://savrn.com/models/swin-poc-1) [Jules Rostand](https://savrn.com/model-publishers/jules1998) Open weights [View model](https://savrn.com/models/swin-poc-1) Model ### [sy-test-adapter-0918](https://savrn.com/models/sy-test-adapter-0918) [Sheng Yao](https://savrn.com/model-publishers/sheng-yao) Open weights apache-2.0 [View model](https://savrn.com/models/sy-test-adapter-0918) Model ### [symbolic-recursion-coherence](https://savrn.com/models/symbolic-recursion-coherence) [Donte Lightfoot](https://savrn.com/model-publishers/dontelightfoot) Symbolic Recursion Coherence is Donte Lightfoot’s open research stack for measuring and steering coherence in complex systems. It is not a drop-in LLM weight dump (pytorchmodel.bin / safetensors chat fine-tune) and not an adapter of Llama, Kimi, DeepSeek, or any other checkpoint. It is a runnable kernel + proof suite + physics DSL + RF telemetry bridge that agents and humans can execute locally, in the Gradio Space, or via MCP. Do not put pipelinetag: text-generation, libraryname: gradio, or basemodel in this file. Those fields are what made the Hub draw a fake model tree (DeepSeek-R1 adapter) and an Inference widget with nothing to serve. Space config (sdk, appfile) belongs on the Space… Open weights mit [View model](https://savrn.com/models/symbolic-recursion-coherence) Model ### [synapse-sr](https://savrn.com/models/synapse-sr) [SharadhNaidu](https://savrn.com/model-publishers/sharadhnaidutrains) Open weights cc0-1.0 [View model](https://savrn.com/models/synapse-sr) Model ### [synas-1-checkpoints](https://savrn.com/models/synas-1-checkpoints) [Finley Thompson](https://savrn.com/model-publishers/thewarrior01) Open weights [View model](https://savrn.com/models/synas-1-checkpoints) Model ### [szl-formulas](https://savrn.com/models/szl-formulas) [SZL Holdings](https://savrn.com/model-publishers/szlholdings) GitHub source: szl-holdings/szl-formulas @ 977f344ccf248b55299a623d91db162f266eda29. Artifacts: BENCH.laptop-blackwell.json, OPERATIONAL.json. Software kernel for SZL formula composition. Not a model. No weights. IS: a pure-Python, stdlib-only governance kernel — offline replay of the 21 canonical formulas plus the governed-loop composer. Public surface includes registrycount(), lambdaaggregate, LOCKEDPROVENFORMULAIDS, rungovernedloop, PROOFSTATUS (mirrored verbatim). IS NOT: trained weights, LoRA, GGUF,.pt, or a classifier. Hub model.joblib is QUARANTINED executable serialization (and is not on this tree) — do not joblib.load it. Not a CUDA/Triton speedup. Not lutar-lean and not the… Open weights apache-2.0 kernels [View model](https://savrn.com/models/szl-formulas) Model · Text classification ### [szl-triage-linear-development-v1](https://savrn.com/models/szl-triage-linear-development-v1) [SZL Holdings](https://savrn.com/model-publishers/szlholdings) NOTPROMOTABLE. No production admission or runtime integration. This is a newly trained, independently implemented six-class Python softmax classifier with 744 train-only features. It is a custom JSON weight format, not a Transformers checkpoint or a hosted inference endpoint. The complete study source, original weights, records, negative control and sealed thresholds are distributed from immutable Git blobs. SOURCEBINDING.json lists their hashes; unsigned hashes require trust in the declared canonical source. The fixed seed 11 study trained on 527 engine-labelled rows, excluding 101 held rows. On the previously exposed 42-row development challenge, joint label/state accuracy was 8/42… Open weights apache-2.0 [View model](https://savrn.com/models/szl-triage-linear-development-v1) Model ### [t-burma](https://savrn.com/models/t-burma) [Myanmar Pyi Ai Techno](https://savrn.com/model-publishers/aitechno-burma) Open weights [View model](https://savrn.com/models/t-burma) ## 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.