What GPU do I need to run facebook/dinov3-vitl16-pretrain-lvd1689m?
303M parameters, published in F32. View on Hugging FaceGated
Set up dinov3-vitl…retrain-lvd1689mdinov3-vitl16-pretrain-lvd1689m is published by facebook on Hugging Face, with 686,008 downloads and 599 likes to date. It's a DINOv3ViTModel model built for image-feature-extraction, published natively in F32, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 1.1 GB | 1.4 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.3 GB | 0.3 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 0.1 GB | 0.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run dinov3-vitl16-pretrain-lvd1689m at its published (F32) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
dinov3-vitl16-pretrain-lvd1689m: common questions
How much VRAM does dinov3-vitl16-pretrain-lvd1689m need?
1.4 GB at FP32, 0.3 GB at FP8 (quantized), 0.2 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 1.1 GB of weights plus inference overhead is the whole requirement.
Do I need approval to download dinov3-vitl16-pretrain-lvd1689m?
Yes. facebook gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 1.4 GB the model needs once you have them.
Can dinov3-vitl16-pretrain-lvd1689m run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 1.1 GB, or 1.4 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 0.6 GB, or 0.7 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of dinov3-vitl16-pretrain-lvd1689m fit on one V100?
11, by VRAM alone. That card carries 16.0 GB and one copy needs 1.4 GB at FP32, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 11 copies is not 11 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More facebook models
- sam3 (860M, F32)
- seamless-m4t-v2-large (2.3B, F32)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
