Model reference · open weights

sapiens-pretrain

Embeddings facebook Image embed 1 build Non-commercial 511 dl/mo

sapiens-pretrain is an open-weight embedding model from Meta. sapiens-pretrain-0.3b (BF16) weighs 1.2 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.

  • Sapiens-pretrain is a 0.3B parameter vision transformer developed by Meta for image feature extraction.
  • It was pretrained on 300 million human images at 1024 x 1024 resolution and supports 1K high-resolution inference.
  • The model is licensed under Creative Commons Attribution-NonCommercial 4.0 and is designed for English language tasks.

Summary of the facebook/sapiens-pretrain-0.3b model card, 2026-10-28

What it is

Released byMeta
Released2024-09-10
VRAM1.2 GB for the weights

What it runs on

Memory and cards for sapiens-pretrain-0.3b (BF16)

1.2 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 Meta says about sapiens-pretrain

Read the model card

Model Details

Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-0.3B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic.

  • Developed by: Meta
  • Model type: Vision Transformer
  • License: Creative Commons Attribution-NonCommercial 4.0
  • Task: pretrain
  • Format: original
  • File: sapiens_0.3b_epoch_1600_clean.pth

Model Card

  • Image Size: 1024 x 1024
  • Num Parameters: 0.336 B
  • FLOPs: 1.242 TFLOPs
  • Patch Size: 16 x 16
  • Embedding Dimensions: 1024
  • Num Layers: 24
  • Num Heads: 16
  • Feedforward Channels: 4096

More Resources

Uses

Pretrained 0.3B model can be used for feature extraction, fine-tuning, or as a starting point for training new models.

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

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together, which is the basis of search and RAG.
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