Model reference · open weights

LibreDINOv3vith16plus

NEW · this week Embeddings LibreYOLO Image embed 1 build Its own licence terms 2k dl/mo

LibreDINOv3vith16plus is an open-weight embedding model from LibreYOLO. LibreDINOv3vith16plus (BF16) weighs 1.7 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • LibreDINOv3vith16plus is an image feature extraction model with 841M parameters, created by LibreYOLO as a mirror of Meta's DINOv3 ViT-H+/16 encoder.
  • The weights are stored in bfloat16 precision and are intended for use with the Speridlabs Idol image-to-3D model.
  • The model is distributed under Meta's DINOv3 License, which includes specific restrictions on use and redistribution.

Summary of the LibreYOLO/LibreDINOv3vith16plus model card, 2026-11-06

What it is

Released byLibreYOLO
Released2026-10-30
Parameters841M
VRAM1.7 GB for the weights

What it runs on

Memory and cards for LibreDINOv3vith16plus (BF16)

1.7 GBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What LibreYOLO says about LibreDINOv3vith16plus

Read the model card

A mirror of Meta's DINOv3 ViT-H+/16 image encoder, facebook/dinov3-vith16plus-pretrain-lvd1689m (pretrained on LVD-1689M), stored in bfloat16 for LibreYOLO.

LibreYOLO's Speridlabs Idol image-to-3D model reads its input through this encoder. The mirror lets LibreGen("speridlabs-idol") run without a Hugging Face login or an access request.

This is a mirror, not a new model

The weights are Meta's. Nothing was retrained, distilled or fine-tuned. The only change is precision: every float32 tensor of the upstream checkpoint was cast to bfloat16 (round to nearest even), the precision Speridlabs Idol runs the encoder in. config.json records torch_dtype: bfloat16; no other field changed.

  • Upstream repository: facebook/dinov3-vith16plus-pretrain-lvd1689m
  • Upstream revision: c807c9eeea853df70aec4069e6f56b28ddc82acc
  • Upstream model.safetensors SHA-256: 3e1d4d18b9bfa9f28fad8e9de6a783f1313532d3460efa4cd0b12521d81d1a4d
  • This model.safetensors SHA-256: 34252ee11eeebf00d50c528d53d725672f03ad88ff30cb724ddf3920bf67dc4c

Loaded with transformers in bfloat16, this checkpoint and the upstream one hold identical tensors (all 615) and give bit-identical outputs.

License: read this before use

These weights are not covered by LibreYOLO's MIT license. They are redistributed under Meta's DINOv3 License, included here as LICENSE. The license permits redistribution on the condition that it is passed through: the weights may only be distributed under the same agreement, with a copy of it. Using or distributing them binds you to it.

Restrictions that pass through, summarized but not replacing the text:

  • Use must comply with applicable laws, including Trade Controls and privacy and data protection law, and you must not be the target of Trade Controls.
  • No activities subject to ITAR or end uses prohibited by Trade Controls, including military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.
  • No reverse engineering or decompiling of the materials.
  • Research publications that use these weights must acknowledge them.
  • Meta may modify the agreement, with changes effective immediately.

Meta gates its own repository behind an access form. This mirror is not gated; users who need Meta's own access process should obtain the weights from the upstream repository.

Usage

With LibreYOLO, which downloads it automatically:

from libreyolo import LibreGen

LibreGen("speridlabs-idol")("chair.jpg").save("chair.ply")

With transformers:

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("LibreYOLO/LibreDINOv3vith16plus", dtype=torch.bfloat16)

Citation

@misc{simeoni2025dinov3,
  title={{DINOv3}},
  author={Sim{\'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\"e}l and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and J{\'e}gou, Herv{\'e} and Labatut, Patrick and Bojanowski, Piotr},
  year={2025},
  eprint={2508.10104},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2508.10104},
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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