Model reference · open weights
gpt2-small-dutch is an open-weight language model from GroNLP. gpt2-small-dutch (FP32) weighs 239 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the GroNLP/gpt2-small-dutch model card, 2026-10-01
What it is
| Released by | GroNLP |
|---|---|
| Released | 2022-03-02 |
| Parameters | 129M |
| VRAM | 239 MB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 1K each | |||
| RTX 3060 12 GB | 287 | all 1K | 11.6 GB |
| RTX 4060 Ti 16 GB | 388 | all 1K | 15.4 GB |
| RTX 3090 24 GB | 600 | all 1K | 23.4 GB |
| RTX 4090 24 GB | 598 | all 1K | 23.4 GB |
| RTX 5090 32 GB | 801 | all 1K | 31.0 GB |
| L40S 48 GB | 1000+ | all 1K | 44.0 GB |
| A100 80 GB | 1000+ | all 1K | 78.2 GB |
| H100 80 GB | 1000+ | all 1K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 1000+ | all 1K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 1000+ | all 1K | 107 GB |
| H200 141 GB | 1000+ | all 1K | 138 GB |
| B200 180 GB | 1000+ | all 1K | 176 GB |
| Requests at once | 1K tokens each |
|---|---|
| 1 | 785 MB |
| 5 | 936 MB |
| 8 | 1.0 GB |
| 16 | 1.4 GB |
| 32 | 2.0 GB |
| 64 | 3.2 GB |
One card, with vLLM's small-card settings.
From the model card
Wietse de Vries • Malvina Nissim
This model is based on the small OpenAI GPT-2 (gpt2) model.
For details, check out our paper on arXiv and the code on Github.
gpt2-small-dutch-embeddings: Small model size with only retrained lexical embeddings.gpt2-small-dutch: Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)gpt2-medium-dutch-embeddings: Medium model size with only retrained lexical embeddings.gpt2-small-italian-embeddings: Small model size with only retrained lexical embeddings.gpt2-small-italian: Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)gpt2-medium-italian-embeddings: Medium model size with only retrained lexical embeddings.from transformers import pipeline
pipe = pipeline("text-generation", model="GroNLP/gpt2-small-dutch")
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-small-dutch")
model = AutoModel.from_pretrained("GroNLP/gpt2-small-dutch") # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/gpt2-small-dutch") # Tensorflow
@misc{devries2020good,
title={As good as new. How to successfully recycle English GPT-2 to make models for other languages},
author={Wietse de Vries and Malvina Nissim},
year={2020},
eprint={2012.05628},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.