# dinov3-vitl16-pretrain-lvd1689m by Camenduru: Open Model Source: https://savrn.com/models/dinov3-vitl16-pretrain-lvd1689m Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve dinov3-vitl16-pretrain-lvd1689m (303M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.6 GB | 0.7 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.3 GB | 0.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [dinov3-vitl16-pretrain-lvd1689m on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/dinov3-vitl16-pretrain-lvd1689m/gpus) ## SAVRN's Notes on dinov3-vitl16-pretrain-lvd1689m What does a vision model that returns features instead of answers earn its rack space with? For dinov3-vitl16-pretrain-lvd1689m the answer is embeddings: it turns a picture into a feature vector that a downstream classifier or search index consumes. At 303 million parameters and 24 layers, the 16-bit weights are 0.6 GB and need 0.7 GB of memory; 8-bit drops that to 0.4 GB. One MI300X at $1.85 an hour is the cheapest card in our table and far more than the model needs, so the decision is about images per hour, not fitting weights. The license field reads other with no summary attached, and the listed publisher is Camenduru while the weights derive from facebook/dinov3-vit7b16-pretrain-lvd1689m, so trace the terms to that source before any commercial deployment. The paper is arXiv:2508.10104. There is no context length to size, so your check is image throughput per card. SAVRN Research, 2026-09-18 ## Model Card DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: - 10 models pretrained on web data (LVD-1689M dataset) - 1 ViT-7B trained from scratch, - 5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B, - 4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B, - 2 models… Excerpt from the card by Camenduru, licensed other. ## Configuration Architecture DINOv3ViTModel Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 RoPE base 100 Stored precision float32 Model type dinov3_vit ## Identity and Version Repository camenduru/dinov3-vitl16-pretrain-lvd1689m Publisher Camenduru Task Image feature extraction Modality Other Library transformers Parameters 303M parameters Languages en Revision 3c276edd87d6f6e569ff0c4400e086807d0f3881 First published 2025-12-17 Last updated 2025-12-17 ## Files and Weights 6 files, 1.2 GB in total. The weights are 1 file totalling 1.2 GB in safetensors. Weights1 file · 1.2 GB Configuration2 files · 1.3 KB Documentation2 files · 22.0 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.2 GB | dcb2e45127cc | | config.json | Configuration | 745 B | — | | preprocessor_config.json | Configuration | 585 B | — | | LICENSE.md | Documentation | 7.5 KB | — | | README.md | Documentation | 14.5 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License other Access Open weights, no gate Download size 1.2 GB [Download from Camenduru](https://huggingface.co/camenduru/dinov3-vitl16-pretrain-lvd1689m) Released by Camenduru through its official repository on Hugging Face. ## Built From - Derived from facebook/dinov3-vit7b16-pretrain-lvd1689m - Described by arXiv:2508.10104 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.2 GB | | 16-bit | 0.6 GB | | 8-bit | 0.3 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare dinov3-vitl16-pretrain-lvd1689m - [dinov2-base vs dinov3-vitl16-pretrain-lvd1689m](https://savrn.com/models/compare/dinov2-base-vs-dinov3-vitl16-pretrain-lvd1689m) ## Questions About dinov3-vitl16-pretrain-lvd1689m ### How much GPU memory does dinov3-vitl16-pretrain-lvd1689m need? About 0.7 GB at 16-bit and 0.2 GB at 4-bit: the weights (303M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run dinov3-vitl16-pretrain-lvd1689m on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### What license is dinov3-vitl16-pretrain-lvd1689m released under? other, as its publisher declares it. Read the license text before commercial use. ## Similar Models Model · Image feature extraction ### [dinov2-large](https://savrn.com/models/dinov2-large) [AI at Meta](https://savrn.com/model-publishers/facebook) Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository. Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion. Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One… Open weights apache-2.0 304M parameters transformers [View model](https://savrn.com/models/dinov2-large) Model · Image feature extraction ### [keural-vision-encoder-mid](https://savrn.com/models/keural-vision-encoder-mid) [Hika Dalju](https://savrn.com/model-publishers/hikaf143) A 183.5M-parameter image encoder built from scratch — adaptive tokenization, hierarchical concept tokens, and continuous 2D positional encoding. Not a CLIP/ViT fine-tune. Measured at 384×384 with tokenbudget=512, matching training. Accuracy trails same-size peers — see Limitations and Evaluation for why, and for the higher-k retrieval results where the gap closes. The encoder is frozen and used as-is inside a bilingual (English + Korean) Only a projector (8.9M) and LoRA adapters (5.0M) are trained on top; the decoder is Qwen2.5-7B-Instruct. A vision-language model can score respectably while barely using the image, because many questions are answerable from language priors alone. Each… Open weights other 184M parameters transformers [View model](https://savrn.com/models/keural-vision-encoder-mid) Model · Image feature extraction ### [dinov2-base](https://savrn.com/models/dinov2-base) [AI at Meta](https://savrn.com/model-publishers/facebook) Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository. Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion. Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One… Open weights apache-2.0 87M parameters transformers [View model](https://savrn.com/models/dinov2-base) Model · Image feature extraction ### [vit-base-patch16-224-in21k](https://savrn.com/models/vit-base-patch16-224-in21k) [Google](https://savrn.com/model-publishers/google) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a… Open weights apache-2.0 86M parameters transformers [View model](https://savrn.com/models/vit-base-patch16-224-in21k) Model · Image feature extraction ### [dinov2-small](https://savrn.com/models/dinov2-small) [AI at Meta](https://savrn.com/model-publishers/facebook) Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository. Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion. Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One… Open weights apache-2.0 22M parameters transformers [View model](https://savrn.com/models/dinov2-small) Model · Image feature extraction ### [vit_small_patch14_dinov2.lvd142m](https://savrn.com/models/vit-small-patch14-dinov2-lvd142m) [PyTorch Image Models](https://savrn.com/model-publishers/timm) A Vision Transformer (ViT) image feature model. Pretrained on LVD-142M with self-supervised DINOv2 method. - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 Explore the dataset and runtime metrics of this model in timm model results. Open weights apache-2.0 22M parameters timm [View model](https://savrn.com/models/vit-small-patch14-dinov2-lvd142m) ## Camenduru [All models and datasets](https://savrn.com/model-publishers/camenduru) ## Versions - [3c276edd87d6](https://savrn.com/models/dinov3-vitl16-pretrain-lvd1689m/versions/3c276edd87d6) · current 2026-09-18 ## Explore More - [All image feature extraction models](https://savrn.com/models/tasks/image-feature-extraction) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/camenduru/dinov3-vitl16-pretrain-lvd1689m) - [How the hub is built](https://savrn.com/model-hub/methodology)