# Models, Page 146 of 537 Source: https://savrn.com/models/page/146 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. 12,870Models 3,655Datasets 291Papers 7,691Publishers 12,980Sourced relationships Updated 2026-10-24 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,582](https://savrn.com/models?task=Text+generation) - [Image and text to text588](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics403](https://savrn.com/models?task=Robotics) - [Feature extraction400](https://savrn.com/models?task=Feature+extraction) - [Text classification335](https://savrn.com/models?task=Text+classification) - [Text to image236](https://savrn.com/models?task=Text+to+image) - [Speech recognition190](https://savrn.com/models?task=Speech+recognition) - [Text to speech148](https://savrn.com/models?task=Text+to+speech) - [Image classification140](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 learning102](https://savrn.com/models?task=Reinforcement+learning) - [Token classification101](https://savrn.com/models?task=Token+classification) - [Zero-shot classification96](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation90](https://savrn.com/models?task=Image+segmentation) - [Object detection87](https://savrn.com/models?task=Object+detection) - [Translation82](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 - [transformers3,042](https://savrn.com/models?library=transformers) - [peft369](https://savrn.com/models?library=peft) - [diffusers210](https://savrn.com/models?library=diffusers) - [pytorch206](https://savrn.com/models?library=pytorch) - [gguf188](https://savrn.com/models?library=gguf) - [mlx172](https://savrn.com/models?library=mlx) - [lerobot154](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp47](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx42](https://savrn.com/models?library=onnx) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h329](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [coreml23](https://savrn.com/models?library=coreml) - [exllamav323](https://savrn.com/models?library=exllamav3) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,428](https://savrn.com/models?modality=Other) - [Text2,883](https://savrn.com/models?modality=Text) - [Image and text648](https://savrn.com/models?modality=Image+and+text) - [Image604](https://savrn.com/models?modality=Image) - [Control505](https://savrn.com/models?modality=Control) - [Audio487](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-2.03,395](https://savrn.com/models?license=apache-2.0) - [mit1,335](https://savrn.com/models?license=mit) - [other812](https://savrn.com/models?license=other) - [cc-by-nc-4.0215](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0172](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-4.056](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) - [llama3.129](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++18](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 weights12,434](https://savrn.com/models?access=open_access) - [Access requested at publisher436](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan116](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 Yuan66](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) - [Christopher Subia-Waud60](https://savrn.com/models?publisher=cwaud) - [Yurun Yuan60](https://savrn.com/models?publisher=yyuan244) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Szekely53](https://savrn.com/models?publisher=attila115) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [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) 12,870 models, sorted by most downloaded. Model · Robotics ### [CAMP-RMBench-Checkpoints](https://savrn.com/models/camp-rmbench-checkpoints) [Kuancheng Wang](https://savrn.com/model-publishers/harrywang01) CAMP (Compressed Action Memory Policy) gives a visuomotor policy a behavioral memory. A recurrent memory is pretrained to reconstruct a compressed (DCT) summary of the robot's own past actions, so its hidden state has to encode what the robot already did; a Diffusion Policy is then conditioned on that state. This lets the policy track task progress and learn from its own failed attempts, which a memoryless policy cannot do when the current image does not determine the next action. This repository holds the CAMP policies for all nine tasks of RMBench, a memory-dependent bimanual manipulation benchmark built on RoboTwin 2.0 (Aloha-AgileX dual-arm robot). Every policy is trained from the… Open weights mit [View model](https://savrn.com/models/camp-rmbench-checkpoints) Model ### [CAMulator](https://savrn.com/models/camulator) [Charlie Becker](https://savrn.com/model-publishers/charlie-becker) Open weights mit [View model](https://savrn.com/models/camulator) Model ### [CASE-8B-RL](https://savrn.com/models/case-8b-rl) [CASE](https://savrn.com/model-publishers/case-bench) Open weights mit [View model](https://savrn.com/models/case-8b-rl) Model ### [CASE-8b](https://savrn.com/models/case-8b) [Minye Shao](https://savrn.com/model-publishers/vinyeh) Open weights cc-by-4.0 [View model](https://savrn.com/models/case-8b) Model ### [CAT-UT](https://savrn.com/models/cat-ut) [Avrova Donz](https://savrn.com/model-publishers/avrovadonz) This repository contains QPR artifacts for the dense, unpruned openbmb/MiniCPM5-2B model. The quantizer uses dense int4 weights with group size 128 and GPTQ calibration; it does not apply 2:4 pruning. The recommended file is dense-int4-distill2000-v2-best.qpr. The minicpm5-2b-dense-int4-g128.qpr file is the matching dense-int4 baseline. Each QPR file is about 1.30 GB and is stored with Git LFS. The candidate was selected from a 2,000-update distillation run starting at the dense-int4 baseline. It was selected at update 775 and independently reloaded before scoring. On 100 held-out Chinese and English prompts with 512 new tokens, direct gpt-6-sol judging gave 39 baseline wins, 58 candidate… Open weights mpl-2.0 [View model](https://savrn.com/models/cat-ut) Model · Text generation ### [CAT-YOKO](https://savrn.com/models/cat-yoko) [Avrova Donz](https://savrn.com/model-publishers/avrovadonz) YOCO 式因果 encoder-decoder MoE。从 MiniCPM5-2B 上采样：openbmb/MiniCPM5-2B-Base（Apache-2.0，Llama GQA）。 libraryname: transformers 只表示 tokenizer / 分片约定走 HuggingFace 生态。当前图是仓库里的 catyoko PyTorch 实现，不是 Hub 上可 AutoModelForCausalLM 直接加载的架构。 50B token 信封上的理论墙钟，不是实测。B200 / SM100 允许的线性 GEMM 走 TeNvfp4Linear（TE NVFP4BlockScaling；B0 冻 encoder 走 FPROP，WGRAD 留给 B1/B2）。无 TE / sm120 时 Nvfp4Linear E2M1/16 仿真。attn softmax / SDPA 仍 fp32。 发布信封 进行中（DummyStream，尚未跑完 8e9）。Vast B200 已回收（2026-09-19）。本文件是释放前快照，不是终局。GitHub 口径：docs/STATUS.md。 代码与指针：GitHub AvrovaDonz2026/CAT-YOKO（不用 LFS）。 这些 overlay 里，checkpoints/b0/ 与 checkpoints/b0-nvfp4-try/ 不是 8B token 信封（只是 --try）。checkpoints/b0-full/trainable.pt 是发布信封 进行中 的 B0… Open weights apache-2.0 transformers [View model](https://savrn.com/models/cat-yoko) Model · Text generation ### [CEDR](https://savrn.com/models/cedr) [Xiang Cheng](https://savrn.com/model-publishers/xc91) CEDR generates reasoning solutions with continuous latent diffusion and a learned prompt encoder. This repository contains six selected-weight inference packages for GSM8K, MATH500, and coding, before and after NFT. The training and inference code and installation instructions are at chengxiang/CEDR. All use async clocks (2.5, 2, 1.5), CFG 1, batch size 32, and a 1,024-token diffusion canvas. The GSM8K post-NFT preset caps decoded responses at 767 tokens. Other presets use the canvas available after each prompt. See the code repository's evaluation guide for MATH500 and official HumanEval/MBPP base/plus scoring. Coding generation uses the combined 164 HumanEval and 378 MBPP tasks. Standard… Open weights apache-2.0 cedr [View model](https://savrn.com/models/cedr) Model · Image classification ### [CIRCLE](https://savrn.com/models/circle) [Wangjiayu](https://savrn.com/model-publishers/uii-open-source) Access requested at publisher cc-by-nc-sa-4.0 transformers [View model](https://savrn.com/models/circle) Model ### [CLIP-LIT](https://savrn.com/models/clip-lit) [Extra](https://savrn.com/model-publishers/juanum) Open weights [View model](https://savrn.com/models/clip-lit) Model · Image classification ### [CLIP-ViT-base-patch32-FT-Cars](https://savrn.com/models/clip-vit-base-patch32-ft-cars) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-cars) Model · Image classification ### [CLIP-ViT-base-patch32-FT-DTD](https://savrn.com/models/clip-vit-base-patch32-ft-dtd) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-dtd) Model · Image classification ### [CLIP-ViT-base-patch32-FT-EuroSAT](https://savrn.com/models/clip-vit-base-patch32-ft-eurosat) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-eurosat) Model · Image classification ### [CLIP-ViT-base-patch32-FT-GTSRB](https://savrn.com/models/clip-vit-base-patch32-ft-gtsrb) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-gtsrb) Model · Image classification ### [CLIP-ViT-base-patch32-FT-MNIST](https://savrn.com/models/clip-vit-base-patch32-ft-mnist) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-mnist) Model · Image classification ### [CLIP-ViT-base-patch32-FT-RESISC45](https://savrn.com/models/clip-vit-base-patch32-ft-resisc45) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-resisc45) Model · Image classification ### [CLIP-ViT-base-patch32-FT-SUN397](https://savrn.com/models/clip-vit-base-patch32-ft-sun397) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-sun397) Model · Image classification ### [CLIP-ViT-base-patch32-FT-SVHN](https://savrn.com/models/clip-vit-base-patch32-ft-svhn) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-ft-svhn) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-Cars](https://savrn.com/models/clip-vit-base-patch32-smat-cars) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-cars) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-DTD](https://savrn.com/models/clip-vit-base-patch32-smat-dtd) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-dtd) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-GTSRB](https://savrn.com/models/clip-vit-base-patch32-smat-gtsrb) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-gtsrb) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-MNIST](https://savrn.com/models/clip-vit-base-patch32-smat-mnist) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-mnist) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-RESISC45](https://savrn.com/models/clip-vit-base-patch32-smat-resisc45) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-resisc45) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-SUN397](https://savrn.com/models/clip-vit-base-patch32-smat-sun397) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-sun397) Model · Image classification ### [CLIP-ViT-base-patch32-SMAT-SVHN](https://savrn.com/models/clip-vit-base-patch32-smat-svhn) [Yanggangu](https://savrn.com/model-publishers/yanggangu) A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately. Open weights mit [View model](https://savrn.com/models/clip-vit-base-patch32-smat-svhn) ## 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.