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.
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× P4 on akash, at $0.032/hr per GPU ($0.032/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
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