# Models, Page 342 of 489 Source: https://savrn.com/models/page/342 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 ### [gr00t-n1.7-h2-sharpa-tele-right-handstate-rel-ckpt40000](https://savrn.com/models/gr00t-n1-7-h2-sharpa-tele-right-handstate-rel-ckpt40000) [Yunju Li](https://savrn.com/model-publishers/yunju1115) Open weights [View model](https://savrn.com/models/gr00t-n1-7-h2-sharpa-tele-right-handstate-rel-ckpt40000) Model ### [gr00t-n1.7-libero-90-k4-causal_vlm_lora_layer_selection](https://savrn.com/models/gr00t-n1-7-libero-90-k4-causal-vlm-lora-layer-selection) [Minjae Lee](https://savrn.com/model-publishers/mjleee) Open weights [View model](https://savrn.com/models/gr00t-n1-7-libero-90-k4-causal-vlm-lora-layer-selection) Model ### [gr00t-n1.7-libero-90-k4-recap](https://savrn.com/models/gr00t-n1-7-libero-90-k4-recap) [Minjae Lee](https://savrn.com/model-publishers/mjleee) Open weights [View model](https://savrn.com/models/gr00t-n1-7-libero-90-k4-recap) Model ### [gr00t-n1.7-libero-90-k4-single-relation-causal-lr5e-5](https://savrn.com/models/gr00t-n1-7-libero-90-k4-single-relation-causal-lr5e-5) [Mango Cloud](https://savrn.com/model-publishers/mangocloud) Open weights [View model](https://savrn.com/models/gr00t-n1-7-libero-90-k4-single-relation-causal-lr5e-5) Model ### [gr00t-n1.7-libero-90-k4-single-relation-causal-vlm-rank8](https://savrn.com/models/gr00t-n1-7-libero-90-k4-single-relation-causal-vlm-rank8) [Mango Cloud](https://savrn.com/model-publishers/mangocloud) Open weights 3.2B parameters [View model](https://savrn.com/models/gr00t-n1-7-libero-90-k4-single-relation-causal-vlm-rank8) Model ### [gr00t-n1.7-libero-90-k4-vlm-causal](https://savrn.com/models/gr00t-n1-7-libero-90-k4-vlm-causal) [Minjae Lee](https://savrn.com/model-publishers/mjleee) Open weights [View model](https://savrn.com/models/gr00t-n1-7-libero-90-k4-vlm-causal) Model ### [gr00t-n1.7-libero-90-k4-vlm-single-relation](https://savrn.com/models/gr00t-n1-7-libero-90-k4-vlm-single-relation) [Minjae Lee](https://savrn.com/model-publishers/mjleee) Open weights [View model](https://savrn.com/models/gr00t-n1-7-libero-90-k4-vlm-single-relation) Model ### [gr00t-n1.7-so101-bimanual-pickvials-2dcam-exp](https://savrn.com/models/gr00t-n1-7-so101-bimanual-pickvials-2dcam-exp) [PoHan Huang](https://savrn.com/model-publishers/nickhuang323) Open weights [View model](https://savrn.com/models/gr00t-n1-7-so101-bimanual-pickvials-2dcam-exp) Model ### [gr00t-n15-b601-can-bowl](https://savrn.com/models/gr00t-n15-b601-can-bowl) [Rami Mouro](https://savrn.com/model-publishers/ramalamadingdong) Open weights 2.7B parameters [View model](https://savrn.com/models/gr00t-n15-b601-can-bowl) Model ### [gr00t-n15-libero-spatial-ppo-fp32head-paperretry-150steps](https://savrn.com/models/gr00t-n15-libero-spatial-ppo-fp32head-paperretry-150steps) [Minjae Oh](https://savrn.com/model-publishers/riasok) Open weights [View model](https://savrn.com/models/gr00t-n15-libero-spatial-ppo-fp32head-paperretry-150steps) Model · Robotics ### [gr00t-n17-bigenlight-50per-15hz-eef-observed-delta-bc10k-headonly-20260926](https://savrn.com/models/gr00t-n17-bigenlight-50per-15hz-eef-observed-delta-bc10k-headonly-20260926) [Junhyeong](https://savrn.com/model-publishers/rlobot-jun) 15 Hz, 10,000 BC updates, freshly initialized from NVIDIA GR00T-N1.7-3B. Labels are future observed/executed TCP poses, not reconstructed robot command poses. No SVF, critic, LoRA, or prior Bigenlight BC initialization. gripper command. For a chunk observed at time t, native relative processing uses inv(T[t]) @ T[t+k+1], k=0..15: local-frame xyz and the first two rows of the relative rotation matrix, flattened row-major. All 16 EEF targets share T[t] as their reference; they are not consecutive-step deltas. Gripper target k retains the original absolute command at t+k, with 0=open and 1=closed. State: 16 dimensions = six measured arm joints in radians, measured gripper, and current observed… Open weights other [View model](https://savrn.com/models/gr00t-n17-bigenlight-50per-15hz-eef-observed-delta-bc10k-headonly-20260926) Model ### [gr00t-trocar-cotrain-tele108-ego107-eef-camright-0921-right](https://savrn.com/models/gr00t-trocar-cotrain-tele108-ego107-eef-camright-0921-right) [Yunju Li](https://savrn.com/model-publishers/yunju1115) Open weights [View model](https://savrn.com/models/gr00t-trocar-cotrain-tele108-ego107-eef-camright-0921-right) Model ### [gr00t-trocar-tele108-eef-camright-0921-right](https://savrn.com/models/gr00t-trocar-tele108-eef-camright-0921-right) [Yunju Li](https://savrn.com/model-publishers/yunju1115) Open weights [View model](https://savrn.com/models/gr00t-trocar-tele108-eef-camright-0921-right) Model · Robotics ### [gr00t1.5n_libero10_x06](https://savrn.com/models/gr00t1-5n-libero10-x06) [Jian Kim](https://savrn.com/model-publishers/jiankimr) Fine-tuned GR00T-N1.5 victim policy trained on sequentially perturbed LIBERO-10 demonstrations. Open weights apache-2.0 2.7B parameters [View model](https://savrn.com/models/gr00t1-5n-libero10-x06) Model ### [gr00tn1.5-libero-spatial-correct-spatial](https://savrn.com/models/gr00tn1-5-libero-spatial-correct-spatial) [Rahul Chand](https://savrn.com/model-publishers/chand0320) Open weights [View model](https://savrn.com/models/gr00tn1-5-libero-spatial-correct-spatial) Model · Robotics ### [gr00tn1.5-libero-spatial-trajectory-efficiency](https://savrn.com/models/gr00tn1-5-libero-spatial-trajectory-efficiency) [Miguel Villa Floran](https://savrn.com/model-publishers/kaweees) This repository contains checkpoints and evaluation artifacts for GR00T fine-tuning. Each epoch folder is a separate model checkpoint; the repository root is an index. - Same 50 total LIBERO Spatial trajectories for every version (5,971 frames). - Vision encoder, language model, and the full action head/DiT are trainable. - Eight epochs maximum; 125 optimizer updates per epoch. - Every epoch checkpoint is uploaded and hash-verified before local weight eviction. - Every checkpoint is evaluated on all ten Spatial tasks, with 50 fixed initial states per task: 500 rollouts. - Success-rate plots use completed simulator evaluations, not training losses. Checkpoints and evaluations appear as the… Open weights [View model](https://savrn.com/models/gr00tn1-5-libero-spatial-trajectory-efficiency) Model ### [grad-cross-modal-fusion](https://savrn.com/models/grad-cross-modal-fusion) [Enzo Souza](https://savrn.com/model-publishers/enzosouza01) A structured set of research notes on Cross Modal Fusion, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - 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 summary.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, hardware, and… Open weights mit 16,576 parameters 512 tokens [View model](https://savrn.com/models/grad-cross-modal-fusion) Model · Text generation ### [grafema-tinyllama-servicios](https://savrn.com/models/grafema-tinyllama-servicios) [Efrain Jared Ramirez Diaz](https://savrn.com/model-publishers/jaredramirezdiaz) 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). - PEFT 0.19.1 Open weights peft [View model](https://savrn.com/models/grafema-tinyllama-servicios) Model ### [graft-smollm2-135m-no-intervention](https://savrn.com/models/graft-smollm2-135m-no-intervention) [Luciaquirke](https://savrn.com/model-publishers/luciaquirke) Open weights [View model](https://savrn.com/models/graft-smollm2-135m-no-intervention) Model ### [grammar-de](https://savrn.com/models/grammar-de) [Kotoshu](https://savrn.com/model-publishers/kotoshu) Fully local German grammar correction model for the ≤250 MB weight class (run anywhere, CPU real-time, no network). iterative apply-and-retag decoding, on a microsoft/mdeberta-v3-base encoder. byte-fallback), pruned by weight-truth-verified remap — int8 ONNX gate, 48.5% yield) from typed corruption packs (articles, verb agreement, V2, separable verbs, case government); two-stage differential-LR schedule, byte-identical with HF) + ONNX Runtime; inference is milliseconds per sentence on a laptop CPU. German has no public GEC benchmark we can license for automated scoring, so we report a held-out judge study instead: This is a deliberately conservative corrector: it almost never invents… Open weights cc-by-nc-sa-4.0 onnx [View model](https://savrn.com/models/grammar-de) Model ### [grammar-en](https://savrn.com/models/grammar-en) [Kotoshu](https://savrn.com/model-publishers/kotoshu) An 85.8 MB int8 ONNX token tagger that detects and corrects grammatical errors fully locally — no data leaves the machine. GECToR architecture (Omelianchuk et al. 2020): distilroberta encoder, 5,000-label closed vocabulary ($KEEP / $REPLACE / $APPEND / $DELETE / $TRANSFORM / $MERGE), iterative apply-and-retag decoding (4 rounds). Scored with the official M2 scorer, noalt gold, --maxunchangedwords 2, 4-round iterative decode: 2.9x LanguageTool; runs in ~6 ms/sentence on a laptop CPU through onnxruntime. labels.json (id -> label map — do not rebuild), vocab.json + merges.txt (distilroberta byte-level BPE). label vocab = top 5,000 from the training mix. real-time (~6 ms/sentence), 85.8 MB int8… Open weights cc-by-nc-sa-4.0 onnxruntime [View model](https://savrn.com/models/grammar-en) Model · Text generation ### [granite-4.0-h-small-w4a16-llmcompressor](https://savrn.com/models/granite-4-0-h-small-w4a16-llmcompressor) [AMD](https://savrn.com/model-publishers/amd) ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-small created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-small using LLM Compressor with the GPTQ algorithm. This reduces the model weights from 60.0 GiB to 16.1 GiB on disk (~73% reduction). granite-4.0-h-small is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 72-expert MoE block (top-10 routing) alongside a shared MLP. Two details make this work. The model is… Open weights apache-2.0 32.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/granite-4-0-h-small-w4a16-llmcompressor) Model · Text generation ### [granite-4.0-h-small-w8a8-llmcompressor](https://savrn.com/models/granite-4-0-h-small-w8a8-llmcompressor) [AMD](https://savrn.com/model-publishers/amd) ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-small created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-small using LLM Compressor via the Round-to-Nearest (RTN) algorithm. This reduces the model weights from 60.0 GiB to 30.4 GiB on disk (~49% reduction). granite-4.0-h-small is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 72-expert MoE block (top-10 routing) alongside a shared MLP. The recipe only needs two… Open weights apache-2.0 32.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/granite-4-0-h-small-w8a8-llmcompressor) Model · Text generation ### [granite-4.0-h-tiny-w8a8-llmcompressor](https://savrn.com/models/granite-4-0-h-tiny-w8a8-llmcompressor) [AMD](https://savrn.com/model-publishers/amd) ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-tiny created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-tiny using LLM Compressor via the Round-to-Nearest (RTN) algorithm. This reduces the model weights from 12.9 GiB to 6.6 GiB on disk (~49% reduction). granite-4.0-h-tiny is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 64-expert MoE block alongside a shared MLP. The recipe only needs two ignore entries. lmhead… Open weights apache-2.0 6.9B parameters 131,072 tokens transformers [View model](https://savrn.com/models/granite-4-0-h-tiny-w8a8-llmcompressor) ## 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.