Model reference · open weights
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.
Summary of the LibreYOLO/LibreDINOv3vith16plus model card, 2026-11-06
What it is
| Released by | LibreYOLO |
|---|---|
| Released | 2026-10-30 |
| Parameters | 841M |
| VRAM | 1.7 GB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From 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.
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.
facebook/dinov3-vith16plus-pretrain-lvd1689mc807c9eeea853df70aec4069e6f56b28ddc82accmodel.safetensors SHA-256: 3e1d4d18b9bfa9f28fad8e9de6a783f1313532d3460efa4cd0b12521d81d1a4dmodel.safetensors SHA-256: 34252ee11eeebf00d50c528d53d725672f03ad88ff30cb724ddf3920bf67dc4cLoaded with transformers in bfloat16, this checkpoint and the upstream one
hold identical tensors (all 615) and give bit-identical outputs.
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:
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.
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)
@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.