Open-weight model · Text generation
DCore-Domino-Qwen3-4B-Budget8
by Wingchiuloong wingchiuloong/DCore-Domino-Qwen3-4B-Budget8
DCore-Domino-Qwen3-4B-Budget8 is an open-weight model for text generation from Wingchiuloong. It has 588M parameters. At 16-bit it needs about 1.4 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Parameters588M
Context—
Weights—
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve DCore-Domino-Qwen3-4B-Budget8 (588M 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 | 1.2 GB | 1.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.6 GB | 0.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.3 GB | 0.4 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 Oct 22, 2026.
DCore-Domino-Qwen3-4B-Budget8 on every accelerator the SAVRN Index prices, at every precision
Model Card
Identity and Version
- Repository
- wingchiuloong/DCore-Domino-Qwen3-4B-Budget8
- Publisher
- Wingchiuloong
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 588M parameters
- Languages
- Not stated by the source
- Revision
- d563fb362134d23c4f7a6bd548243e08465d5924
- First published
- 2026-10-22
- Last updated
- 2026-10-22
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
Download from Wingchiuloong
Released by Wingchiuloong through its official repository on Hugging Face.
Built From
- Derived from Qwen/Qwen3-4B
- Described by arXiv:2610.24993
Memory Requirements
| Precision | Weights in memory |
|---|---|
| 16-bit | 1.2 GB |
| 8-bit | 0.6 GB |
| 4-bit | 0.3 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About DCore-Domino-Qwen3-4B-Budget8
How much GPU memory does DCore-Domino-Qwen3-4B-Budget8 need?
About 1.4 GB at 16-bit and 0.4 GB at 4-bit: the weights (588M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run DCore-Domino-Qwen3-4B-Budget8 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.
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This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This model is a fine-tuned version of Qwen/Qwen3-0.6B on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 4 - evalbatchsize: 8 - gradientaccumulationsteps: 16 - totaltrainbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 100 - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.2