# Models, Page 159 of 525
Source: https://savrn.com/models/page/159
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 · Text generation

### [Frontis-MA1-30B-SFT](https://savrn.com/models/frontis-ma1-30b-sft)

[Frontis AI](https://savrn.com/model-publishers/frontisai)

This is the supervised fine-tuning checkpoint of the Frontis-MA1 family, before OpenMLE reinforcement learning. It is intended for research on machine learning engineering and program transformation. The actual 24,350-row training input is distinct from the 26,259-example public SFT trace collection. Dataset cutoff configuration was 32,768 tokens; this does not establish validated long-context inference performance. No standalone SFT benchmark claim is made by this release. The separately released RL model is FrontisAI/Frontis-MA1-30B. Pin the published commit SHA for reproducibility. This model requires substantial memory; use the dependency versions recorded in the release validation…

Open weights cc-by-nc-4.0 30.5B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/frontis-ma1-30b-sft)

Model · Image and text to text

### [Frontis-MA1-35B-SFT](https://savrn.com/models/frontis-ma1-35b-sft)

[Frontis AI](https://savrn.com/model-publishers/frontisai)

This is the supervised fine-tuning checkpoint of the Frontis-MA1 family, before OpenMLE reinforcement learning. It is intended for research on machine learning engineering and program transformation. The actual 24,350-row training input is distinct from the 26,259-example public SFT trace collection. Dataset cutoff configuration was 32,768 tokens; this does not establish validated long-context inference performance. No standalone SFT benchmark claim is made by this release. The separately released RL model is FrontisAI/Frontis-MA1-35B. Pin the published commit SHA for reproducibility. This model requires substantial memory; use the dependency versions recorded in the release validation…

Open weights cc-by-nc-4.0 36B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/frontis-ma1-35b-sft)

Model

### [FuOOhJjGGCZT3guC](https://savrn.com/models/fuoohjjggczt3guc)

[14A3](https://savrn.com/model-publishers/14a3)

Open weights

[View model](https://savrn.com/models/fuoohjjggczt3guc)

Model

### [FunctionGemma-HermesAgent-TitleGenerator](https://savrn.com/models/functiongemma-hermesagent-titlegenerator)

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

A fine-tuned google/functiongemma-270m-it model for naming chat sessions from a user's opening message. It produces a setsessiontitle function call containing a concise title, rather than answering the message. The saved model is in functiongemmatitlemodel/, including full model weights, tokenizer, generation configuration, and chat template. - Generate a title of 3–7 words in sentence case. - Describe the user's task or conversation topic. - Preserve technical terms. - Return only a setsessiontitle tool call. Example output from the saved tuned evaluation: This is FunctionGemma's tool-call syntax, not JSON. The application must parse and validate the call before using the title. Install…

Open weights 268M parameters 32,768 tokens

[View model](https://savrn.com/models/functiongemma-hermesagent-titlegenerator)

Model · Unconditional image generation

### [FuseReg](https://savrn.com/models/fusereg)

[Hongyang Du](https://savrn.com/model-publishers/hongyang-du)

Official checkpoints for FuseReg. FuseReg replaces heuristic encoder-layer fusion in representation autoencoders (RAEs) with training over random subsets of encoder layers, so a single pixel decoder reconstructs reliably under any layer-subset fusion and pairs better with the diffusion generator. The repository currently contains 16 inference checkpoints: eight DINOv3-L k=23 decoders, two DINOv3-L k=7 decoders, and six DiT-XL generators. The k=7 p=0.3, four EUPE, and four SigLIP variants have not been published. A 25-file candidate collection is tracked in the checkpoint manifest; only entries in its files array are available for download. Checkpoint manifest records file size, SHA-256…

Open weights mit pytorch

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

Model

### [Fx-Work-GGUF](https://savrn.com/models/fx-work-gguf)

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

static quants of https://huggingface.co/endless-frontier/Fx-Work For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/fx-work-gguf)

Model

### [G4-26B-SFT-v2-1-GRPO-chkpt40-MN](https://savrn.com/models/g4-26b-sft-v2-1-grpo-chkpt40-mn)

[ApocalypseParty](https://savrn.com/model-publishers/apocalypseparty)

Open weights

[View model](https://savrn.com/models/g4-26b-sft-v2-1-grpo-chkpt40-mn)

Model

### [G4-26B-SFT-v2-1-GRPO-chkpt40-MN-gguf](https://savrn.com/models/g4-26b-sft-v2-1-grpo-chkpt40-mn-gguf)

[ApocalypseParty](https://savrn.com/model-publishers/apocalypseparty)

Open weights

[View model](https://savrn.com/models/g4-26b-sft-v2-1-grpo-chkpt40-mn-gguf)

Model

### [G4-Jamet-26B-A4B-MK-1](https://savrn.com/models/g4-jamet-26b-a4b-mk-1)

[Nakra](https://savrn.com/model-publishers/hastagaras)

Open weights 25.8B parameters 262,144 tokens

[View model](https://savrn.com/models/g4-jamet-26b-a4b-mk-1)

Model

### [G4-Jamet-26B-A4B-MK-1-i1-GGUF](https://savrn.com/models/g4-jamet-26b-a4b-mk-1-i1-gguf)

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

weighted/imatrix quants of https://huggingface.co/Hastagaras/G4-Jamet-26B-A4B-MK-1 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/G4-Jamet-26B-A4B-MK-1-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…

Open weights transformers

[View model](https://savrn.com/models/g4-jamet-26b-a4b-mk-1-i1-gguf)

Model

### [G4-WanaMeroQueen-v3-31B-heretic-i1-GGUF](https://savrn.com/models/g4-wanameroqueen-v3-31b-heretic-i1-gguf)

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

weighted/imatrix quants of https://huggingface.co/vulture3/G4-WanaMeroQueen-v3-31B-heretic For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/G4-WanaMeroQueen-v3-31B-heretic-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/g4-wanameroqueen-v3-31b-heretic-i1-gguf)

Model · Image to video

### [GAE-D64-1B](https://savrn.com/models/gae-d64-1b)

[ARC Lab, Tencent](https://savrn.com/model-publishers/tencentarc)

(hiddensize=[768, 2048], depth=[28, 6]) over a 64-channel geometry-native latent, at 672×378 and V=81, with metric Plücker camera conditioning and Qwen3-0.6B text. This repo is self-contained for GAE-64 inference: codec, per-channel latent statistics, DA3 feature-level stats, and the flow transformer. The frozen DA3-GIANT encoder / DPT head comes from Or, after the files are in ckpts/: Image-to-video uses 50 Euler steps and CFG 2 with one reference view. - codec/ — safetensors + config.json - gae64.pt — drop-in checkpoint for gae.loadcodec - latentstatsgae64.pt — per-channel latent mean/std - da3statsgiant5ds.tar — DA3 feature-level normalization stats - transformer/ — flow safetensors +…

Open weights pytorch

[View model](https://savrn.com/models/gae-d64-1b)

Model

### [GATOR](https://savrn.com/models/gator)

[Bowen Wen](https://savrn.com/model-publishers/bowen1nvidia)

Weights for the three-stage GATOR cascade, which reconstructs a textured mesh from several masked RGB views of one object. Each.statedict.ckpt holds only the fp32 model statedict of that training step, without optimizer or EMA state. Each config.yaml is the training configuration of its run; its dataset paths are not used at inference. t5gemma-2-1b-1b-text-only-dd0a268/ holds the text encoder of google/t5gemma-2-1b-1b at revision dd0a2683227859151b1730ca3a63087df5b5f39b: the model.encoder. weights in bfloat16 plus the tokenizer. taoyuantextencodermanifest.json records the export. Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms; see the…

Access requested at publisher other

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

Model

### [GDN_pico_ByT5_tokenizer](https://savrn.com/models/gdn-pico-byt5-tokenizer)

[Kurogane Himaki](https://savrn.com/model-publishers/kurogane)

use attahed notebook.

Open weights apache-2.0 10M parameters 2,048 tokens

[View model](https://savrn.com/models/gdn-pico-byt5-tokenizer)

Model

### [GGUF-gemma4-SFT-26-10-19-03-29](https://savrn.com/models/gguf-gemma4-sft-26-10-19-03-29)

[Aj](https://savrn.com/model-publishers/stellarconduct)

Open weights

[View model](https://savrn.com/models/gguf-gemma4-sft-26-10-19-03-29)

Model

### [GKb8BBL70Mykicbi](https://savrn.com/models/gkb8bbl70mykicbi)

[14A3](https://savrn.com/model-publishers/14a3)

Open weights

[View model](https://savrn.com/models/gkb8bbl70mykicbi)

Model

### [GLM-4.7-Flash-Hauhau-Aggressive-W6A8](https://savrn.com/models/glm-4-7-flash-hauhau-aggressive-w6a8)

[Cassidy Barton](https://savrn.com/model-publishers/xananthium)

Native compressed-tensors W6A8 checkpoint published by Xananthium. Credit and upstream terms: HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive. The source model card is preserved in READMEORIGINAL.md. Quantization provenance and retained high-precision components are recorded in conversion-receipt.json. I wanted W6A8, so that is what we built: six-bit stored weights, dynamic eight-bit activations, and vLLM doing the serving. Humming packs the INT6 values across INT32 word boundaries and unpacks them for the 3090's supported INT8 matrix math. The cards do not need a native six-bit Tensor Core instruction. The extra unpacking and scale work is part of the cost, and the speed tests…

Open weights mit 29.9B parameters 202,752 tokens vllm

[View model](https://savrn.com/models/glm-4-7-flash-hauhau-aggressive-w6a8)

Model · Text generation

### [GLM-5.3](https://savrn.com/models/glm-5-3-2)

[SAIFI INDUSTRIES](https://savrn.com/model-publishers/saifiindustries)

GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: GLM-5.3 supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide - For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here. - GLM-5.3 supports controlling the thinking budget through the reasoningeffort parameter, which accepts three levels: low, high, and max. It defaults to max…

Open weights other 753.3B parameters 1,048,576 tokens transformers

[View model](https://savrn.com/models/glm-5-3-2)

Model · Text generation

### [GLM-5.3](https://savrn.com/models/glm-5-3-3)

[Vaibhav](https://savrn.com/model-publishers/vaibhavhome30)

GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: GLM-5.3 supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide - For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here. - GLM-5.3 supports controlling the thinking budget through the reasoningeffort parameter, which accepts three levels: low, high, and max. It defaults to max…

Open weights other 753.3B parameters 1,048,576 tokens transformers

[View model](https://savrn.com/models/glm-5-3-3)

Model · Text generation

### [GLM-5.3-DSpark](https://savrn.com/models/glm-5-3-dspark)

[Alaya NeW](https://savrn.com/model-publishers/alayanew)

This repository releases a DSpark draft model for GLM-5.3. It is a speculator, not a standalone LLM: pair it with a GLM-5.3 target (BF16 or FP8) in SGLang or vLLM. Requires a recent SGLang or vLLM build with native DSpark support. This draft is for GLM-5.3 only. --speculative-num-draft-tokens is block size + 1. The draft is bf16 dense. Keep the draft KV cache dtype on auto so it does not inherit the target's FP8 MLA KV cache. - Context-length numbers are the mean of 3 runs (50 prompts, SPEED-Bench low-entropy throughput) - Qualitative numbers are one run on SPEED-Bench categories (80 prompts each) Acceptance length is the per-request mean of completion tokens divided by verification steps.…

Open weights mit 2B parameters 1,048,576 tokens transformers

[View model](https://savrn.com/models/glm-5-3-dspark)

Model

### [GLM-5.3-Flash-AZ-Focus-EXL3](https://savrn.com/models/glm-5-3-flash-az-focus-exl3)

[AxonZeta Ltd](https://savrn.com/model-publishers/axonzeta)

Access requested at publisher

[View model](https://savrn.com/models/glm-5-3-flash-az-focus-exl3)

Model

### [GLM-5.3-Flash-Distill-Qwen-7B-LoRA](https://savrn.com/models/glm-5-3-flash-distill-qwen-7b-lora)

[Sscl](https://savrn.com/model-publishers/sscl)

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/glm-5-3-flash-distill-qwen-7b-lora)

Model

### [GLM-5.3-Flash-EW-MFQ](https://savrn.com/models/glm-5-3-flash-ew-mfq)

[Tylogi](https://savrn.com/model-publishers/tylogi)

Open weights mit

[View model](https://savrn.com/models/glm-5-3-flash-ew-mfq)

Model

### [GLM-5.3-Flash-EXL3](https://savrn.com/models/glm-5-3-flash-exl3)

[Phil Tompkins](https://savrn.com/model-publishers/philbert440)

Access requested at publisher

[View model](https://savrn.com/models/glm-5-3-flash-exl3)

## 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.