This model is a fine-tuned version of Qwen/Qwen3.5-9B-Base on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
Open-weight model · Image and text to text
moondream3.1-9B-A2B
by Moondream moondream/moondream3.1-9B-A2B
Moondream 3.1 is a vision language model with a mixture-of-experts architecture (9B total parameters, 2B active). It delivers state-of-the-art visual reasoning and detection while staying fast and cheap to deploy.
Runs On
What it takes to serve moondream3.1-9B-A2B (9.3B 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 | 18.5 GB | 22.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 9.3 GB | 11.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 4.6 GB | 5.6 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.
Model Card
Moondream 3.1 is a vision language model with a mixture-of-experts architecture (9B total parameters, 2B active). It delivers state-of-the-art visual reasoning and detection while staying fast and cheap to deploy. Skills include query, detect, point, and caption, all native and all returning structured output. For the full story on what's new — including how we trained it and how it holds up on your own tasks — see the release notes. Photon is Moondream's high-performance inference engine. It runs the model locally on NVIDIA GPUs (Ampere or newer) and Apple Silicon Macs, with the same API as Moondream Cloud. No API key is required to run the base model locally. (You'll only need one to run…
Excerpt from the card by Moondream, licensed other.
Identity and Version
- Repository
- moondream/moondream3.1-9B-A2B
- Publisher
- Moondream
- Task
- Image and text to text
- Modality
- Image and text
- Library
- Not stated by the source
- Parameters
- 9.3B parameters
- Languages
- Not stated by the source
- Revision
- 1da27e6c2d2f9118de14560df456881dc62d91f3
- First published
- 2026-06-30
- Last updated
- 2026-07-08
Files and Weights
4 files, 10.5 GB in total. The weights are 1 file totalling 10.5 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 10.5 GB | 26dde16af961 |
| config.json | Configuration | 38 B | — |
| README.md | Documentation | 2.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 10.5 GB
Released by Moondream through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 10.5 GB |
| 16-bit | 18.5 GB |
| 8-bit | 9.3 GB |
| 4-bit | 4.6 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About moondream3.1-9B-A2B
How much GPU memory does moondream3.1-9B-A2B need?
About 22.3 GB at 16-bit and 5.6 GB at 4-bit: the weights (9.3B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run moondream3.1-9B-A2B 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.
What license is moondream3.1-9B-A2B released under?
other, as its publisher declares it. Read the license text before commercial use.
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