mudler/depth-anything.cpp-gguf

Depth Anything 3 — GGUF weights for depth-anything.cpp

Brought to you by the LocalAI team.

GGUF conversions of ByteDance Depth Anything 3, for use with depth-anything.cpp — a from-scratch C++17 / ggml port. No Python, no PyTorch, no CUDA toolkit at inference: one self-contained GGUF file plus a small native library and CLI, faster than PyTorch on CPU and bit-exact against the original (correlation 1.0, verified component by component).

Given an image, the engine recovers a dense depth map, per-pixel confidence, camera extrinsics (3×4) and intrinsics (3×3), an optional sky mask, a back-projected 3D point cloud, and exports to glb / COLMAP / PLY.

Files in this repo

Each GGUF is fully self-contained — every dimension, hyperparameter and preprocessing constant is baked into the file; the loader reads them, nothing is hardcoded.

File	Source checkpoint	Backbone	Depth type	Output
depth-anything-small-f32.gguf	DA3-SMALL	ViT-S	relative	depth + conf + pose
depth-anything-base-f32.gguf	DA3-BASE	ViT-B	relative	depth + conf + pose
depth-anything-base-f16.gguf	DA3-BASE	ViT-B	relative	depth + conf + pose
depth-anything-base-q8_0.gguf	DA3-BASE	ViT-B	relative	depth + conf + pose (near-lossless)
depth-anything-base-q4_k.gguf	DA3-BASE	ViT-B	relative	depth + conf + pose (99 MB)
depth-anything-large-f32.gguf	DA3-LARGE	ViT-L	relative	depth + conf + pose
depth-anything-giant-f32.gguf	DA3-GIANT	ViT-g	relative	depth + conf + pose + 3D Gaussians
depth-anything-mono-large-f32.gguf	DA3MONO-LARGE	ViT-L	relative (monocular)	depth + sky
depth-anything-metric-large-f32.gguf	DA3METRIC-LARGE	ViT-L	metric	metric depth + sky
depth-anything-nested-anyview.gguf	DA3NESTED-GIANT-LARGE (anyview branch)	ViT-g	relative	depth + conf + pose
depth-anything-nested-metric.gguf	DA3NESTED-GIANT-LARGE (metric branch)	ViT-L	metric	depth + sky

The nested model is a two-file pair: the engine loads the anyview (ViT-g) branch and the metric (ViT-L) branch together and aligns them to produce metric-scale depth + pose. Download both depth-anything-nested-anyview.gguf and depth-anything-nested-metric.gguf.

Which one should I use?

• Just trying it out / CPU: depth-anything-base-q4_k.gguf (99 MB, near-lossless).
• Best quality/speed default: depth-anything-base-q8_0.gguf.
• Smallest / fastest: depth-anything-small-f32.gguf.
• Highest quality + 3D reconstruction (point cloud / Gaussians): depth-anything-giant-f32.gguf.
• Single-image depth with sky mask: depth-anything-mono-large-f32.gguf.
• Metric-scale depth (meters), single model: depth-anything-metric-large-f32.gguf.
• Best metric-scale depth + pose: the nested pair (depth-anything-nested-anyview.gguf + depth-anything-nested-metric.gguf).

Usage

depth-anything.cpp (CLI)

git clone https://github.com/mudler/depth-anything.cpp && cd depth-anything.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j

# download a weight from this repo
hf download mudler/depth-anything.cpp-gguf depth-anything-base-q4_k.gguf --local-dir models

./build/da3 depth models/depth-anything-base-q4_k.gguf image.jpg --out depth.png
./build/da3 depth models/depth-anything-base-q4_k.gguf image.jpg --pose poses.json
./build/da3 reconstruct models/depth-anything-giant-f32.gguf image.jpg --ply cloud.ply

# metric-scale depth from the single metric model
./build/da3 depth models/depth-anything-metric-large-f32.gguf image.jpg --out depth.png

# metric-scale depth + pose from the nested pair (anyview + metric branches)
./build/da3 depth models/depth-anything-nested-anyview.gguf image.jpg \
    --metric-model models/depth-anything-nested-metric.gguf --pfm depth.pfm

See the README for multi-view, glb/COLMAP export, quantization and the flat C API.

LocalAI

local-ai run depth-anything-3-base

Performance

Faster than PyTorch on CPU at half the memory, bit-exact. AMD Ryzen 9 9950X3D, threads=16, 504×336, sustained:

engine	quant	model MB	load ms	infer ms	peak RAM MB	vs PyTorch
PyTorch	f32	516	749	416.9	1328	1.00×
C++/ggml	f32	393	112	346.4	614	1.20×
C++/ggml	q8_0	142	40	319.4	363	1.31×
C++/ggml	q4_k	99	25	395.2	320	1.05×

Full methodology in benchmarks/BENCHMARK.md.

License

The GGUF weights are derived from the official Depth Anything 3 checkpoints and inherit their Apache-2.0 license. The depth-anything.cpp code is MIT.

Citation

@article{depthanything3,
  title   = {Depth Anything 3: Recovering the Visual Space from Any Views},
  author  = {ByteDance Seed},
  year    = {2025}
}