Model reference · open weights
codebert is an open-weight embedding model from microsoft. codebert-base (BF16) weighs 499 MB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the microsoft/codebert-base model card, 2026-10-01
What it is
| Released by | microsoft |
|---|---|
| Released | 2022-03-02 |
| VRAM | 499 MB 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
Pretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.
The model is trained on bi-modal data (documents & code) of CodeSearchNet
This model is initialized with Roberta-base and trained with MLM+RTD objective (cf. the paper).
Please see the official repository for scripts that support "code search" and "code-to-document generation".
@misc{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou},
year={2020},
eprint={2002.08155},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.