# Models, Page 207 of 525
Source: https://savrn.com/models/page/207
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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SAVRN Model Hub

# Open-Weight Models

An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.

Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.

12,579Models

3,583Datasets

291Papers

7,578Publishers

12,850Sourced relationships

Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,548](https://savrn.com/models?task=Text+generation)
- [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction398](https://savrn.com/models?task=Feature+extraction)
- [Robotics398](https://savrn.com/models?task=Robotics)
- [Text classification331](https://savrn.com/models?task=Text+classification)
- [Text to image232](https://savrn.com/models?task=Text+to+image)
- [Speech recognition187](https://savrn.com/models?task=Speech+recognition)
- [Text to speech145](https://savrn.com/models?task=Text+to+speech)
- [Image classification139](https://savrn.com/models?task=Image+classification)
- [Audio classification130](https://savrn.com/models?task=Audio+classification)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning)
- [Token classification100](https://savrn.com/models?task=Token+classification)
- [Zero-shot classification94](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation90](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Translation80](https://savrn.com/models?task=Translation)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask69](https://savrn.com/models?task=Fill+mask)
- [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting)
- [Video classification61](https://savrn.com/models?task=Video+classification)
- [Image to text60](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers3,001](https://savrn.com/models?library=transformers)
- [peft367](https://savrn.com/models?library=peft)
- [diffusers206](https://savrn.com/models?library=diffusers)
- [pytorch198](https://savrn.com/models?library=pytorch)
- [gguf187](https://savrn.com/models?library=gguf)
- [mlx169](https://savrn.com/models?library=mlx)
- [lerobot152](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp46](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [minimax-h328](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [coreml22](https://savrn.com/models?library=coreml)
- [exllamav322](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [litert17](https://savrn.com/models?library=litert)
- [litert-lm16](https://savrn.com/models?library=litert-lm)

Modality

- [Other7,212](https://savrn.com/models?modality=Other)
- [Text2,838](https://savrn.com/models?modality=Text)
- [Image and text637](https://savrn.com/models?modality=Image+and+text)
- [Image598](https://savrn.com/models?modality=Image)
- [Control498](https://savrn.com/models?modality=Control)
- [Audio481](https://savrn.com/models?modality=Audio)
- [Video116](https://savrn.com/models?modality=Video)
- [Multimodal79](https://savrn.com/models?modality=Multimodal)
- [Time series64](https://savrn.com/models?modality=Time+series)
- [Tabular56](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,344](https://savrn.com/models?license=apache-2.0)
- [mit1,319](https://savrn.com/models?license=mit)
- [other796](https://savrn.com/models?license=other)
- [cc-by-nc-4.0213](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0164](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [unknown33](https://savrn.com/models?license=unknown)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0)
- [gpl-3.020](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [llama28](https://savrn.com/models?license=llama2)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights12,153](https://savrn.com/models?access=open_access)
- [Access requested at publisher426](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai105](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311)
- [Jessen Pu77](https://savrn.com/models?publisher=putty77)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan62](https://savrn.com/models?publisher=yuruny)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Khazzz1c53](https://savrn.com/models?publisher=khazic)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [NVIDIA50](https://savrn.com/models?publisher=nvidia)
- [Wang49](https://savrn.com/models?publisher=davidwdw)

[Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params)

12,579 models, sorted by most downloaded.

Model · Image and text to text

### [PRTS-Gemma-4-E4B](https://savrn.com/models/prts-gemma-4-e4b)

[SuiDevelopment](https://savrn.com/model-publishers/suilabs)

This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open weights apache-2.0 8B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/prts-gemma-4-e4b)

Model · Text to image

### [PS-IMAGE-1.5](https://savrn.com/models/ps-image-1-5)

[Berbis](https://savrn.com/model-publishers/pedro21613)

LoRA sobre stabilityai/stable-diffusion-xl-base-1.0, evolução incremental da PS-IMAGE-1.4: inicializada com os pesos 1.4 e continuada por +700 steps focados em texto. letras+números, captions em 5 línguas (80 EN / 80 PT / 80 ES / 80 FR / 80 DE) - 700 steps, batch efetivo 4, lr 3e-5 cosine, AdamW, min-SNR γ=5, T4, loss final 0.0057 - Checkpoints a cada 100 steps em checkpoints/step (dá p/ retomar o treino) O SDXL rende melhor em inglês. Jeito mais fiel — tradutor leve + 1.5: A 1.5 também treinou com captions PT/ES/FR/DE, então prompting direto nessas línguas funciona melhor que na 1.4 (ver textpt e textes abaixo) — mas traduzir p/ inglês continua sendo o mais fiel. Prompt (seed 24000): photo…

Open weights diffusers

[View model](https://savrn.com/models/ps-image-1-5)

Model

### [PS-IMAGE-1.6](https://savrn.com/models/ps-image-1-6)

[Berbis](https://savrn.com/model-publishers/pedro21613)

Evolução do PS-IMAGE-1.5 — mesmo formato de LoRA (UNet do SDXL, r16/α32, toq/k/v/out.0, ff.net.0.proj/ff.net.2), treinado por continuação (drop-in: o código do 1.5 funciona trocando o nome do repo). - Comparação A/B: exemplos15/ (1.5) vs exemplos16/ (1.6), mesmos prompts e seeds SDXL não tem entrada de imagem — "visão" não se treina como LoRA. Para editar fotos mantendo consistência use o notebook PS Image 1.6 Edicao: img2img, inpainting por máscara, ControlNet (canny/depth/pose), IP-Adapter (imagem de referência) e refino 768→1152, tudo usando este LoRA.

Open weights

[View model](https://savrn.com/models/ps-image-1-6)

Model

### [PS-IMAGE-2.0](https://savrn.com/models/ps-image-2-0)

[Berbis](https://savrn.com/model-publishers/pedro21613)

Open weights

[View model](https://savrn.com/models/ps-image-2-0)

Model

### [PS-design-2.6](https://savrn.com/models/ps-design-2-6)

[Berbis](https://savrn.com/model-publishers/pedro21613)

Evolução incremental do PS-design-2.5 — fine-tune contínuo do mesmo LoRA (r16 / alpha32, base Qwen/Qwen2.5-0.5B-Instruct), treinado em 2× Tesla T4 em 102 min. reais (SaaS, agência, saúde digital, coaching, pilates, e-commerce, portfólio), convertidos em arquivos HTML únicos e autônomos, com o CSS da própria página embutido em. Cada exemplo real foi repetido ~25× para pesar de verdade ao lado da destilação. - Auto-destilação: 55 páginas geradas pelo próprio 2.5 (30 nichos × 6 design systems), mantendo o estilo do modelo e evitando esquecimento catastrófico. Checkpoints intermediários: checkpoints/checkpoint-. Exemplos gerados: exemplos/.

Open weights

[View model](https://savrn.com/models/ps-design-2-6)

Model

### [PSL_E0_corrected](https://savrn.com/models/psl-e0-corrected)

[Muhammad Usman](https://savrn.com/model-publishers/em-usman25)

Open weights

[View model](https://savrn.com/models/psl-e0-corrected)

Model

### [PSL_E0_corrected_v5](https://savrn.com/models/psl-e0-corrected-v5)

[Muhammad Usman](https://savrn.com/model-publishers/em-usman25)

Open weights

[View model](https://savrn.com/models/psl-e0-corrected-v5)

Model

### [PULSE](https://savrn.com/models/pulse)

[Zhaoyang Jia](https://savrn.com/model-publishers/zhaoyangjia)

PULSE is an asymmetric, variable-rate neural image codec for practical compression on resource-constrained hardware. It combines ultra-low-complexity decoding, bit-exact entropy coding, and MSE or perceptual optimization. provide deterministic entropy transport, with a content-adaptive Meta Prior. levels each, including face/text-aware perceptual fine-tuning. decompression, and optimized CPU, H100 GPU, and mobile NPU deployment tools. Each bundle contains config.json, model.pt and entropycontrolint.pt. Eight QPs (0–7) are supported. Keep the matching files together. manifest.json and SHA256SUMS record all inference-file checksums. Install the PULSE code following its installation guide…

Open weights mit pytorch

[View model](https://savrn.com/models/pulse)

Model

### [PaBV](https://savrn.com/models/pabv)

[Razer](https://savrn.com/model-publishers/razer112)

Open weights mit

[View model](https://savrn.com/models/pabv)

Model

### [PaddleOCR](https://savrn.com/models/paddleocr)

[Nguyễn Văn Hùng](https://savrn.com/model-publishers/hungvladimir)

Open weights

[View model](https://savrn.com/models/paddleocr)

Model

### [Pallete-2.6B-RP-Reasoning-2609-v1b-GGUF](https://savrn.com/models/pallete-2-6b-rp-reasoning-2609-v1b-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

static quants of https://huggingface.co/Indexnusrefather/Pallete-2.6B-RP-Reasoning-2609-v1b For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pallete-2.6B-RP-Reasoning-2609-v1b-i1-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Open weights other transformers

[View model](https://savrn.com/models/pallete-2-6b-rp-reasoning-2609-v1b-gguf)

Model

### [Pallete-2.6B-RP-Reasoning-2609-v1b-i1-GGUF](https://savrn.com/models/pallete-2-6b-rp-reasoning-2609-v1b-i1-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

weighted/imatrix quants of https://huggingface.co/Indexnusrefather/Pallete-2.6B-RP-Reasoning-2609-v1b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Pallete-2.6B-RP-Reasoning-2609-v1b-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Open weights other transformers

[View model](https://savrn.com/models/pallete-2-6b-rp-reasoning-2609-v1b-i1-gguf)

Model · Image and text to text

### [Peppercorn-0.3b](https://savrn.com/models/peppercorn-0-3b)

[Ta Khanh Duy](https://savrn.com/model-publishers/duyta)

Peppercorn is a compact vision-language model for Vietnamese and English document OCR, with 328M parameters. It takes a document page as input and produces structured Markdown, including HTML tables and LaTeX mathematical expressions. Its name comes from the Vietnamese saying nhỏ nhưng có võ — small, but mighty: compact in size, yet capable. Compared with original design, Peppercorn uses approximately 4× fewer image tokens per page (~900 vs. ~3,600) and achieves higher throughput under our tested serving configuration. It performs particularly well on Vietnamese administrative documents and synthetic tables, while English OCR, mathematical expressions, and some real-world financial tables…

Open weights 328M parameters 16,384 tokens transformers

[View model](https://savrn.com/models/peppercorn-0-3b)

Model

### [Perception-Aware-MMR-Experiments](https://savrn.com/models/perception-aware-mmr-experiments)

[HABIBUR RAHMAN](https://savrn.com/model-publishers/habib1295)

Open weights

[View model](https://savrn.com/models/perception-aware-mmr-experiments)

Model

### [Perlis](https://savrn.com/models/perlis)

[Nguyen Hoang Minh Duc](https://savrn.com/model-publishers/minhduc26121)

Open weights mit

[View model](https://savrn.com/models/perlis)

Model · Feature extraction

### [PhaGen](https://savrn.com/models/phagen)

[Tim Trump](https://savrn.com/model-publishers/tim419)

PhaGen-0404-step12800 is the frozen paper model: 152,421,601 parameters, three hierarchical stages with dimensions 512/256/196, maximum token canvas 131,072, and a nine-token character-level DNA tokenizer. This repository contains the standalone inference weights, tokenizer, model configuration and custom code. Weights are the selected April 2026 run's global step 12,800, not the final saved checkpoint or a later training implementation. Use PyTorch 2.5, Transformers 4.54.1, einops 0.8, beartype 0.22, accelerate and safetensors. The PhaGen source repository also provides an installable phagen CLI and an agent skill. Pin a Hub commit hash with revision for repeatable loading. Custom model…

Open weights 152M parameters transformers

[View model](https://savrn.com/models/phagen)

Model

### [Phi-4-mini-instruct-DSpark](https://savrn.com/models/phi-4-mini-instruct-dspark)

[Yosef Worku Alemneh](https://savrn.com/model-publishers/rasyosef)

Open weights 589M parameters

[View model](https://savrn.com/models/phi-4-mini-instruct-dspark)

Model · Text classification

### [Phocinae-Largha-150M-v1](https://savrn.com/models/phocinae-largha-150m-v1)

[Phocinae](https://savrn.com/model-publishers/phocinae)

Tiny model. Big decisions. — 小海豹，大决断。· 一斑见全豹，一点定全局。 斑海豹 Largha, the spotted seal: a 144.3M bilingual decision model (150M-class) for structured decisions — one forward pass per decision, on your own hardware. Not a chat model: it takes a state plus a list of typed questions (noul yes/no · choice pick-one · score 2–10) and returns calibrated answers with confidence, robust to option reordering. The official runtime is phocinae-server: a local FastAPI service (127.0.0.1 only) that loads these weights with a pure PyTorch forward (no extra runtime needed). Full spec: docs/protocol.md. answers values: noul = bool · choice = 0-based option index · score = integer 2–10. Errors: 422 (unknown…

Open weights apache-2.0 144M parameters

[View model](https://savrn.com/models/phocinae-largha-150m-v1)

Model

### [Phoebus_AD](https://savrn.com/models/phoebus-ad)

[Mulemp](https://savrn.com/model-publishers/mulemp)

Access requested at publisher

[View model](https://savrn.com/models/phoebus-ad)

Model

### [Phoenix-Pretrain](https://savrn.com/models/phoenix-pretrain)

[Anyverse Dynamics](https://savrn.com/model-publishers/anyversedynamics)

Open weights

[View model](https://savrn.com/models/phoenix-pretrain)

Model

### [Phoenix-Pretrain](https://savrn.com/models/phoenix-pretrain-2)

[OpenPhoenix-Team](https://savrn.com/model-publishers/openphoenix)

Open weights

[View model](https://savrn.com/models/phoenix-pretrain-2)

Model

### [Photo_Anime](https://savrn.com/models/photo-anime)

[001555](https://savrn.com/model-publishers/jcak009555)

The Photo Anime model is based on Neural Network Lora Fusion (Layered Lora Fusion). Fusion Method: This process involves a layered merging of various Lora modules, focusing on highly minute and precise stratification of different Lora characteristics. Refinement: After the layered fusion, a "surgical adjustment" is performed using the underlying neural network. Result: This technique achieves unprecedented levels of imagination, artistic coherence, practicality, speed, and ease of use. User Experience: Its efficacy is demonstrated by the model's ability to imbue the generated image with "soul" using only the simplest prompt: 1girl. Cultural commentators and artists find it difficult to…

Access requested at publisher

[View model](https://savrn.com/models/photo-anime)

Model · Image and text to text

### [PhysicalEye-Decide-35B](https://savrn.com/models/physicaleye-decide-35b)

[Physical AI](https://savrn.com/model-publishers/masterset-ai)

One forward pass. One calibrated decision. PhysicalEye-Decide-35B answers a question about an image by choosing among the options you give it, and returns a probability for every option. It does not write text. It reads the image, the question and the options once, and outputs a decision. Use it for visual inspection, yes/no checks, option selection and scoring on production lines, in apps and in robots. - A small decision readout sits on top of the backbone and scores each option directly. One request carries up to 4 images, an optional context (state) and one or more questions. Three question types are supported: choice (pick one option), noul (yes or no) and score (a value on a scale).…

Open weights apache-2.0 34.6B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/physicaleye-decide-35b)

Model

### [PicoPolite240M-base](https://savrn.com/models/picopolite240m-base)

[Reiwa-AI K.K.](https://savrn.com/model-publishers/reiwa-ai)

PicoPolite240M is a small-scale language model created using extremely limited computational resources (a single GPU with 32 GB memory) to research training methods for LLMs and SLMs. We will publish a book detailing the entire learning methodology. Furthermore, we will release the complete synthetic dataset created for this purpose. Using the data below, I trained the model for one epoch with a token window of 2000 and fp32 precision; the RoPE θ was set to 3200. Subsequently, using the data below, I trained it for one epoch with a token window of 6400 and bf16 precision; the RoPE θ was set to 10000. Afterward, I applied LongRoPE to extrapolate the token window. This model utilizes google…

Open weights gemma 237M parameters 51,200 tokens

[View model](https://savrn.com/models/picopolite240m-base)

## 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.