grounding-dino-tiny by IDEA-Research: Open-Weight Model

Runs On

What it takes to serve grounding-dino-tiny (172M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	0.3 GB	0.4 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.2 GB	0.2 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.1 GB	0.1 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 grounding-dino-tiny

How many copies fit on one card is the question grounding-dino-tiny raises, not whether one fits. This 172M-parameter detector finds objects in an image from a text description rather than a fixed label set, and it needs 0.4 GB of memory at 16-bit, 0.2 GB at 8-bit, 0.1 GB at 4-bit. On the cheapest Index setup, one 192 GB MI300X at $1.85 an hour, it is a rounding error, so the real question is whether it belongs on a data center GPU or next to the cameras.

Apache 2.0 clears most deployment questions: commercial use, modification and redistribution are permitted if you keep the notices and state significant changes. Stored precision is float32, so the 1.38 GB you fetch is larger than what you serve. Confirm the checkpoint, last updated May 12, 2024, matches the paper describing it, arXiv 2303.05499. No per-token host price applies.

SAVRN Research, 2026-09-18

Model Card

By IDEA-Research, published under apache-2.0, revision a2bb814dd30d.

Grounding DINO model (tiny variant)

The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot.

Grounding DINO overview. Taken from the original paper.

Intended uses & limitations

You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data).

How to use

Here's how to use the model for zero-shot object detection:

Read the full model card (259 words)

Configuration

Architecture: GroundingDinoForObjectDetection
Stored precision: float32
Model type: grounding-dino

Identity and Version

Repository: IDEA-Research/grounding-dino-tiny
Publisher: IDEA-Research
Task: Zero shot object detection
Modality: Other
Library: transformers
Parameters: 172M parameters
Languages: Not stated by the source
Revision: a2bb814dd30d776dcf7e30523b00659f4f141c71
First published: 2023-09-25
Last updated: 2024-05-12

Files and Weights

11 files, 1.4 GB in total. The weights are 2 files totalling 1.4 GB in bin, safetensors.

Weights2 files · 1.4 GB

Configuration4 files · 2.3 KB

Tokenizer3 files · 944.1 KB

Documentation1 file · 2.6 KB

Repository1 file · 1.5 KB

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	689.4 MB	1a2412ef99bd
pytorch_model.bin	Weights	691.9 MB	f4d69c8403b9
added_tokens.json	Configuration	82 B	—
config.json	Configuration	1.6 KB	—
preprocessor_config.json	Configuration	457 B	—
special_tokens_map.json	Configuration	125 B	—
README.md	Documentation	2.6 KB	—
.gitattributes	Repository	1.5 KB	—
tokenizer.json	Tokenizer	711.4 KB	—
tokenizer_config.json	Tokenizer	1.2 KB	—
vocab.txt	Tokenizer	231.5 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 1.4 GB

Download from IDEA-Research

Released by IDEA-Research through its official repository on Hugging Face. Read the license.

Built From

Described by arXiv:2303.05499

Memory Requirements

Precision	Weights in memory
As published	1.4 GB
16-bit	0.3 GB
8-bit	0.2 GB
4-bit	0.1 GB

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

Questions About grounding-dino-tiny

How much GPU memory does grounding-dino-tiny need?

About 0.4 GB at 16-bit and 0.1 GB at 4-bit: the weights (172M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run grounding-dino-tiny 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 grounding-dino-tiny commercially?

Yes. grounding-dino-tiny 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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