# Models, Page 409 of 477
Source: https://savrn.com/models/page/409
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.

11,442Models

3,247Datasets

285Papers

6,976Publishers

12,091Sourced relationships

Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,413](https://savrn.com/models?task=Text+generation)
- [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction341](https://savrn.com/models?task=Feature+extraction)
- [Robotics338](https://savrn.com/models?task=Robotics)
- [Text classification303](https://savrn.com/models?task=Text+classification)
- [Text to image209](https://savrn.com/models?task=Text+to+image)
- [Speech recognition171](https://savrn.com/models?task=Speech+recognition)
- [Audio classification157](https://savrn.com/models?task=Audio+classification)
- [Image classification137](https://savrn.com/models?task=Image+classification)
- [Text to speech132](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 learning93](https://savrn.com/models?task=Reinforcement+learning)
- [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation88](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)
- [Translation78](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)
- [Video classification59](https://savrn.com/models?task=Video+classification)
- [Image to text58](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,777](https://savrn.com/models?library=transformers)
- [peft345](https://savrn.com/models?library=peft)
- [diffusers187](https://savrn.com/models?library=diffusers)
- [pytorch178](https://savrn.com/models?library=pytorch)
- [gguf167](https://savrn.com/models?library=gguf)
- [lerobot139](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [mlx136](https://savrn.com/models?library=mlx)
- [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)
- [vllm38](https://savrn.com/models?library=vllm)
- [onnx36](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)
- [exllamav316](https://savrn.com/models?library=exllamav3)
- [openpi16](https://savrn.com/models?library=openpi)
- [litert15](https://savrn.com/models?library=litert)
- [rkllm15](https://savrn.com/models?library=rkllm)

Modality

- [Other6,473](https://savrn.com/models?modality=Other)
- [Text2,612](https://savrn.com/models?modality=Text)
- [Image and text570](https://savrn.com/models?modality=Image+and+text)
- [Image565](https://savrn.com/models?modality=Image)
- [Audio478](https://savrn.com/models?modality=Audio)
- [Control431](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-2.03,105](https://savrn.com/models?license=apache-2.0)
- [mit1,246](https://savrn.com/models?license=mit)
- [other703](https://savrn.com/models?license=other)
- [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause)
- [gemma62](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.049](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)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [unknown27](https://savrn.com/models?license=unknown)
- [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-m11](https://savrn.com/models?license=creativeml-openrail-m)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [llama27](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 weights11,087](https://savrn.com/models?access=open_access)
- [Access requested at publisher355](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai94](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 Pu69](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)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [Microsoft48](https://savrn.com/models?publisher=microsoft)
- [Yurun Yuan48](https://savrn.com/models?publisher=yuruny)
- [NVIDIA46](https://savrn.com/models?publisher=nvidia)
- [Khazzz1c44](https://savrn.com/models?publisher=khazic)
- [Quan44](https://savrn.com/models?publisher=wq2012)
- [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)

11,442 models, sorted by most downloaded.

Model

### [qwen3-8b-swe-lego-essential-ckpt](https://savrn.com/models/qwen3-8b-swe-lego-essential-ckpt)

[Si1w](https://savrn.com/model-publishers/si1w)

Open weights

[View model](https://savrn.com/models/qwen3-8b-swe-lego-essential-ckpt)

Model

### [qwen3-8b-swe-lego-llm-ckpt](https://savrn.com/models/qwen3-8b-swe-lego-llm-ckpt)

[Si1w](https://savrn.com/model-publishers/si1w)

Open weights

[View model](https://savrn.com/models/qwen3-8b-swe-lego-llm-ckpt)

Model

### [qwen3-8b-swe-lego-llm-essential-ckpt](https://savrn.com/models/qwen3-8b-swe-lego-llm-essential-ckpt)

[Si1w](https://savrn.com/model-publishers/si1w)

Open weights

[View model](https://savrn.com/models/qwen3-8b-swe-lego-llm-essential-ckpt)

Model · Text generation

### [qwen3-8b-worker-farca](https://savrn.com/models/qwen3-8b-worker-farca)

[Atlas Institute of Mechatronics](https://savrn.com/model-publishers/atlas-institute)

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/qwen3-8b-worker-farca)

Model · Speech recognition

### [qwen3-asr-1.7b-glade](https://savrn.com/models/qwen3-asr-1-7b-glade)

[Josh Leverette](https://savrn.com/model-publishers/coder543)

Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. Glade conversion of Qwen3-ASR-1.7B by the Qwen Team, Alibaba Cloud, at source revision 7278e1e70fe206f11671096ffdd38061171dd6e5. The converted weights retain Apache-2.0 licensing; see LICENSE and NOTICE. This conversion is not endorsed by the upstream authors. This repository contains one offline transcription bundle, approximately 2.68 GB, using 30-second chunks and up to eight independent decoder lanes. It prefers ANE for both the audio encoder and…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-asr-1-7b-glade)

Model · Speech recognition

### [qwen3-asr-1.7b-sherpa-onnx-int8-4096](https://savrn.com/models/qwen3-asr-1-7b-sherpa-onnx-int8-4096)

[Shige Dondon](https://savrn.com/model-publishers/shigedonsan)

A Windows x64 CPU package for offline transcription with sherpa-onnx. The decoder has a statically exported 4096-position KV cache. It uses a mixed INT8/FP32 decoder and retains the matching FP16 encoder and convolution frontend from the pinned sherpa-compatible ONNX export. - maxtotallen: 4096; tested maxnewtokens: 1024 The encoder is kept FP16 because the compatible INT8 encoder from a separate 512-context export substantially reduced Japanese recognition quality in a controlled comparison. On the same FLEURS Japanese sample, the mixed decoder with the FP16 encoder measured 8.2% character error rate (CER), compared with 5.5% for the all-FP16 source and 22.7% when using that separate INT8…

Open weights apache-2.0 sherpa-onnx

[View model](https://savrn.com/models/qwen3-asr-1-7b-sherpa-onnx-int8-4096)

Model

### [qwen3-asr-bola-stt-full-dataset](https://savrn.com/models/qwen3-asr-bola-stt-full-dataset)

[Chinmay Choudhary](https://savrn.com/model-publishers/chinmay-choudhary)

Access requested at publisher

[View model](https://savrn.com/models/qwen3-asr-bola-stt-full-dataset)

Model

### [qwen3-embedding-8b-govinfo-retriever](https://savrn.com/models/qwen3-embedding-8b-govinfo-retriever)

[Vamsee Krishna](https://savrn.com/model-publishers/vamsee9201)

Open weights

[View model](https://savrn.com/models/qwen3-embedding-8b-govinfo-retriever)

Model

### [qwen3-forced-aligner-glade](https://savrn.com/models/qwen3-forced-aligner-glade)

[Josh Leverette](https://savrn.com/model-publishers/coder543)

Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. Glade conversion of Qwen3 ForcedAligner, source revision c07281df297b9905d24a508279258cccf987a064. The upstream model and converted weights use Apache License 2.0. The complete checkpoint has approximately 918 million parameters; the 0.6B name describes its text backbone. This model predicts timestamps for supplied words. It does not recognize or replace a transcript. Requires physical Apple silicon with macOS 27 or iOS 27, Core AI APIs, and Glade's…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-forced-aligner-glade)

Model

### [qwen3-harness-finetune](https://savrn.com/models/qwen3-harness-finetune)

[Satoshi Sasahara](https://savrn.com/model-publishers/papandada)

This model was finetuned and converted to GGUF format using Unsloth. - Qwen3.5-9B.Q4KM.gguf - Qwen3.5-9B.F16-mmproj.gguf This was trained 2x faster with Unsloth

Open weights 262,144 tokens

[View model](https://savrn.com/models/qwen3-harness-finetune)

Model

### [qwen3-omni-saudi-voice-encrypted](https://savrn.com/models/qwen3-omni-saudi-voice-encrypted)

[Aa](https://savrn.com/model-publishers/waeluf)

Encrypted experimental release payload for a custom Talker/MTP LoRA layer over Qwen3-Omni. This repository payload contains encrypted step96 adapter artifacts only: - encrypted/talkermtplora.safetensors.aes256gcm - encrypted/talkermtplora.safetensors.aes256gcm.nonce - encrypted/talkermtplora.safetensors.aes256gcm.tag - decryptlocal.ps1 - DECRYPTGUIDE.md - encryptiontestreport.json - RELEASEMANIFEST.sha256.txt It does not include plaintext weights, the decryption key, private local notes, restored test outputs, temporary failure-test outputs, or the full training project. This release is for experimental review and verification only. Commercial use is not permitted without explicit written…

Open weights other

[View model](https://savrn.com/models/qwen3-omni-saudi-voice-encrypted)

Model

### [qwen3-opd-experiment-archive](https://savrn.com/models/qwen3-opd-experiment-archive)

[Yufan Zhang](https://savrn.com/model-publishers/yufanzh)

52 complete FP32 checkpoints for Qwen3-1.7B PG-OPD with Qwen3-4B-Instruct-2507 teacher. Full seeds 0/1/2/3 at steps 5/10/15/20; Fisher/Random q=0.5%, 1%, 5% at step 20, Random mask seeds 0/1. Download an entire runs/ /checkpoint- / directory (weights, config, tokenizer). Optimizer states are not included. Checkpoint files were preserved without reupload. All 17 Test1 sets and the remaining research records are in the final public dataset snapshot: https://huggingface.co/datasets/yufanzh/qwen3-opd-records-20260926/tree/e60404a2dfcebf402da94441f9a1d831d22fddd3 Current HEAD contains checkpoints plus README/catalog. Earlier mixed-archive history and all local originals are preserved. The…

Open weights

[View model](https://savrn.com/models/qwen3-opd-experiment-archive)

Model

### [qwen3-sft-feedback-with-proof-repair-rl-200-unmasked](https://savrn.com/models/qwen3-sft-feedback-with-proof-repair-rl-200-unmasked)

[Formalmathatepfl](https://savrn.com/model-publishers/formalmathatepfl)

Open weights 8.2B parameters 32,768 tokens

[View model](https://savrn.com/models/qwen3-sft-feedback-with-proof-repair-rl-200-unmasked)

Model · Text to speech

### [qwen3-tts-base-onnx](https://savrn.com/models/qwen3-tts-base-onnx)

[Nile Academy](https://savrn.com/model-publishers/nile-academy)

First-party Nile Academy conversion of the official Qwen checkpoint at fd4b254389122332181a7c3db7f27e918eec64e3. This is a derivative ONNX artifact, not an ONNX release published by Qwen. It contains data only; Python is not required by BookAlive. The fp32 pack includes a speaker encoder, talker with an explicit KV cache, a complete code-predictor frame, speech decoder, embedding tables, tokenizer and publisher licence. BookAlive reuses its Qwen VoiceDesign generation implementation for the Base talker, predictor and decoder. The source implementation is pinned at 022e286b98fbec7e1e916cb940cdf532cd9f488e. The current adapter uses x-vector-only reference cloning. It supports ten languages…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-tts-base-onnx)

Model

### [qwen3-vl-2b-cataract-grpo](https://savrn.com/models/qwen3-vl-2b-cataract-grpo)

[Shahed Momenzadeh](https://savrn.com/model-publishers/shahedm2001)

Open weights

[View model](https://savrn.com/models/qwen3-vl-2b-cataract-grpo)

Model · Image and text to text

### [qwen3-vl-2b-medical-pmcvqa-sft-merged](https://savrn.com/models/qwen3-vl-2b-medical-pmcvqa-sft-merged)

[HarisshRagav](https://savrn.com/model-publishers/gl3mon)

This qwen3vl model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open weights apache-2.0 2.1B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/qwen3-vl-2b-medical-pmcvqa-sft-merged)

Model · Text generation

### [qwen3-vl-4b-vietnamese-sft_v2](https://savrn.com/models/qwen3-vl-4b-vietnamese-sft-v2)

[Luong Van Phuc](https://savrn.com/model-publishers/phucdu)

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 0.20.0

Open weights peft

[View model](https://savrn.com/models/qwen3-vl-4b-vietnamese-sft-v2)

Model

### [qwen3-vl-8b-hebrew-v22b-ckpt](https://savrn.com/models/qwen3-vl-8b-hebrew-v22b-ckpt)

[Isaac Godfried](https://savrn.com/model-publishers/isaacmg)

This model is a fine-tuned version of unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit. It has been trained using TRL. This model was trained with SFT.

Open weights transformers

[View model](https://savrn.com/models/qwen3-vl-8b-hebrew-v22b-ckpt)

Model · Image and text to text

### [qwen3-vl-8b-tomato-cutpoint-cued-rgbd-9mm](https://savrn.com/models/qwen3-vl-8b-tomato-cutpoint-cued-rgbd-9mm)

[Namho Koh](https://savrn.com/model-publishers/namhokaist)

Full fine-tune of Qwen/Qwen3-VL-8B-Instruct (revision 0c351dd) for the box-conditioned variant of the deleafing cut-point task: the input is one robot head-camera frame (848x408 RGB) with its aligned depth map (metres, fed as a rendered second image) and the bounding box of the target petiole; the output is the nominal cut point 9 mm along that petiole from its junction with the main stem. The model does not choose the target. Compared with the box+point models in this account (qwen3-vl-8b-tomato-cutpoint-bp-, which find the petiole themselves), this one measures localization given identity. Answer: {"cutpointuv":[x,y]} in normalized [0,1000) coordinates of the original 848x408 frame (2…

Open weights apache-2.0 770,288 parameters 262,144 tokens

[View model](https://savrn.com/models/qwen3-vl-8b-tomato-cutpoint-cued-rgbd-9mm)

Model

### [qwen3.5](https://savrn.com/models/qwen3-5)

[Priva Studio](https://savrn.com/model-publishers/privastudio)

Open weights 262,144 tokens

[View model](https://savrn.com/models/qwen3-5)

Model · Text generation

### [qwen3.5-0.8b-grammarly](https://savrn.com/models/qwen3-5-0-8b-grammarly)

[Matthew](https://savrn.com/model-publishers/americansquid)

This model is a fine-tuned version of Qwen/Qwen3.5-0.8B. It has been trained using TRL. This model was trained with SFT. - PEFT 0.21.0

Open weights peft

[View model](https://savrn.com/models/qwen3-5-0-8b-grammarly)

Model

### [qwen3.5-0.8b-medical-mlp5-first5](https://savrn.com/models/qwen3-5-0-8b-medical-mlp5-first5)

[Aditya G](https://savrn.com/model-publishers/adityag-india)

Qwen/Qwen3.5-0.8B fully fine-tuned (no LoRA) on FreedomIntelligence/medical-o1-reasoning-SFT (en). Only the MLP blocks of layers [0, 1, 2, 3, 4] were trained; everything else is frozen. Checkpoint: best.pt. Split 80/10/10, 10 epochs, lr 1e-05.

Open weights apache-2.0 752M parameters 262,144 tokens

[View model](https://savrn.com/models/qwen3-5-0-8b-medical-mlp5-first5)

Model

### [qwen3.5-2b-healcare-query](https://savrn.com/models/qwen3-5-2b-healcare-query)

[Dai Pham](https://savrn.com/model-publishers/daipham31)

Open weights mit

[View model](https://savrn.com/models/qwen3-5-2b-healcare-query)

Model · Image and text to text

### [qwen3.5-35b-a3b-instruct-merged8083-sft](https://savrn.com/models/qwen3-5-35b-a3b-instruct-merged8083-sft)

[TR AKR](https://savrn.com/model-publishers/akrtr)

This model is a fine-tuned version of /mnt/shared-storage-user/mineru2-shared/niujunbo/ldy/models/Qwen3.5-35B-A3B-Instruct on the merged8083ulogocohqwen35 dataset. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 1.0 - Transformers 5.6.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

Open weights other 664,944 parameters 262,144 tokens transformers

[View model](https://savrn.com/models/qwen3-5-35b-a3b-instruct-merged8083-sft)

## 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.