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Open-weight model · Depth estimation

Depth-Anything-V2-Small-hf

by Depth Aything depth-anything/Depth-Anything-V2-Small-hf

Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features: - more fine-grained details than Depth Anything V1 - more robust…

Parameters25M
Context—
Weights99.2 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads2.6M

Runs On

What it takes to serve Depth-Anything-V2-Small-hf (25M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.0 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on Depth-Anything-V2-Small-hf

Ninety-nine megabytes on disk is the whole download, and at 16-bit the memory needed rounds to 0.1 GB. The cheapest setup our Index lists, one MI300X with 192 GB at $1.85 per hour on-demand, carries roughly two thousand times that: nobody buys a GPU for this model. Depth Anything V2 Small takes one image and returns a depth map, and it lives in whatever memory is left on a card already doing other work.

Apache 2.0 lets you ship it inside a commercial product, fine-tune it and redistribute the result, provided the notices travel with it and significant changes are stated. Access is open. No context length applies because the input is pixels, not tokens, and no Index host prices it, so it costs only the hardware minutes it borrows. Two arXiv papers describe it, 2406.09414 and 2401.10891, and the checkpoint has not changed since July 5, 2024.

SAVRN Research, 2026-09-18

Model Card

By Depth Aything, published under apache-2.0, revision 5426e4f0f365.

Depth Anything V2 Small – Transformers Version

Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features: - more fine-grained details than Depth Anything V1 - more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard) - more efficient (10x faster) and more lightweight than SD-based models - impressive fine-tuned performance with our pre-trained models

This model checkpoint is compatible with the transformers library.

Read the full model card (430 words)

Configuration

Architecture
DepthAnythingForDepthEstimation
Stored precision
float32
Model type
depth_anything

Identity and Version

Repository
depth-anything/Depth-Anything-V2-Small-hf
Publisher
Depth Aything
Task
Depth estimation
Modality
Other
Library
transformers
Parameters
25M parameters
Languages
Not stated by the source
Revision
5426e4f0f36572d16453bbda7a8389317b1bef99
First published
2024-06-18
Last updated
2024-07-05

Files and Weights

5 files, 99.2 MB in total. The weights are 1 file totalling 99.2 MB in safetensors.

Weights1 file · 99.2 MB
Configuration2 files · 1.7 KB
Documentation1 file · 4.4 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights99.2 MB 3152477ce0d8
config.jsonConfiguration950 B —
preprocessor_config.jsonConfiguration775 B —
README.mdDocumentation4.4 KB —
.gitattributesRepository1.5 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
99.2 MB
Download from Depth Aything

Released by Depth Aything through its official repository on Hugging Face. Read the license.

Built From

  • Described by arXiv:2401.10891
  • Described by arXiv:2406.09414

Memory Requirements

PrecisionWeights in memory
As published99.2 MB
16-bit0.0 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Depth-Anything-V2-Small-hf

How much GPU memory does Depth-Anything-V2-Small-hf need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (25M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Depth-Anything-V2-Small-hf on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use Depth-Anything-V2-Small-hf commercially?

Yes. Depth-Anything-V2-Small-hf is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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