Model reference · open weights
bartpho-word is an open-weight embedding model from vinai. bartpho-word (BF16) weighs 1.7 GB; 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 vinai/bartpho-word model card, 2026-10-01
What it is
| Released by | vinai |
|---|---|
| Released | 2022-03-02 |
| VRAM | 1.7 GB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
Two BARTpho versions BARTpho-syllable and BARTpho-word are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art.
The general architecture and experimental results of BARTpho can be found in our paper:
@article{bartpho,
title = {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}},
author = {Nguyen Luong Tran and Duong Minh Le and Dat Quoc Nguyen},
journal = {arXiv preprint},
volume = {arXiv:2109.09701},
year = {2021}
}
Please CITE our paper when BARTpho is used to help produce published results or incorporated into other software.
For further information or requests, please go to BARTpho's homepage!
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.