VGGT-1B
facebookcc-by-nc-4.0vision

VGGT-1B

Updated Nov 13, 2026
Parameters
1.3B
Context
8,192
License
cc-by-nc-4.0
Updated
Nov 13, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit VGGT-1B with real runtime overhead.
Q4_K_M
1 GB
Q5_K_M
1 GB
Q8_0
1 GB
FP16
3 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve facebook/VGGT-1B

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

VGGT-1B is an open-weight 3D generation model from facebook, published on Hugging Face in March 2025. It has 1.3B parameters and is released under the cc-by-nc-4.0 license.

HardwareLink to this section

At full FP16 precision the weights need about 3 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.

PopularityLink to this section

VGGT-1B had 640,900 downloads on Hugging Face in the last month and has 158 likes.

Run it locallyLink to this section

The weights are on Hugging Face. Follow the model card for the exact setup and license terms.

Frequently asked

Quick answers to common questions

How much VRAM does VGGT-1B need?

VGGT-1B with 1.3B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is VGGT-1B better than other facebook models?

VGGT-1B has 1.3B parameters with 8,192 context - a strong choice for 3d-generation.

What license is VGGT-1B under?

VGGT-1B is released under the cc-by-nc-4.0 license, making it suitable for most commercial and personal projects.

What hardware runs VGGT-1B well?

With 1.3B parameters, VGGT-1B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for VGGT-1B?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with VGGT-1B?

VGGT-1B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec