SAVRN Model Hub · Comparisons
bert-large-uncased vs xlm-roberta-large
Bert-large-uncased has 336M parameters and xlm-roberta-large has 561M parameters; bert-large-uncased is released under Apache License 2.0 and xlm-roberta-large under MIT License; at 16-bit, bert-large-uncased needs about 0.8 GB (1x MI300X from $1.85 an hour) and xlm-roberta-large about 1.3 GB (1x MI300X from $1.85 an hour).
| Field | bert-large-uncased google-bert/bert-large-uncased | xlm-roberta-large FacebookAI/xlm-roberta-large |
|---|---|---|
| Publisher | BERT community | Facebook AI community |
| Task | Fill mask | Fill mask |
| Modality | Text | Text |
| Parameters, as reported | 336M parameters | 561M parameters |
| Architecture | BertForMaskedLM | XLMRobertaForMaskedLM |
| Library | transformers | transformers |
| Context length | 512 tokens | 514 tokens |
| Repository size | 8.2 GB | 11.2 GB |
| Artifact formats | safetensors, pytorch, jax, rust, tf | safetensors, onnx, pytorch, jax, tf |
| License | apache-2.0 | mit |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.8 GB | 1.3 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.2 GB | 0.3 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 6da4b6a26a18 | c23d21b0620b |
| Downloads reported by the hub | 1M | 3.2M |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
SAVRN's Notes on bert-large-uncased
Fill mask is the task on the label, so the pretraining is what you buy and the fine-tune is yours to run. It learned English from BookCorpus and Wikipedia, and it is uncased, so english and English read the same to it. At 336 million parameters the 16-bit weights are 0.7 GB with 0.8 GB memory needed, and our cheapest line is one MI300X at $1.85 per hour, a 192 GB card with room for more than two hundred copies.
Apache 2.0 permits commercial use, modification and redistribution; you keep the license, copyright and NOTICE files, state significant changes, and receive an express patent grant from contributors. Before committing: the model card was not written by the team that released the model, and the file carries no reported evaluations, so your own fine-tune is the only measurement you get. The 512-token context caps input length; the paper is arXiv:1810.04805.
SAVRN's Notes on xlm-roberta-large
Fitting this one is never the problem. At 16-bit the weights are 1.1 GB and the run needs 1.3 GB, so the cheapest setup on the page, one 192 GB MI300X at $1.85 an hour on-demand, has room for more than a hundred copies; put it on a GPU already serving something else. What the footprint buys is a fill-mask model trained on 2.5 TB of filtered CommonCrawl in 100 languages with a 250,002-entry vocabulary: a starting layer for multilingual text work you tune yourself, not a finished application.
MIT asks only that the copyright and permission notice travel with what you ship; commercial use, modification and redistribution are allowed. Note the 514-token context, sentences and short passages rather than documents, and the 17 files totaling 11.2 GB across five formats, of which your runtime needs one. The releasing team did not write the card; start from arXiv 1911.02116.
Questions
Which is larger, bert-large-uncased or xlm-roberta-large?
xlm-roberta-large (561M parameters) is larger than bert-large-uncased (336M parameters), by the parameter counts their publishers report.
Which is cheaper to run, bert-large-uncased or xlm-roberta-large?
At 4-bit, bert-large-uncased fits on 1x MI300X from $1.85 an hour and xlm-roberta-large on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use bert-large-uncased commercially?
Yes. bert-large-uncased is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Can I use xlm-roberta-large commercially?
Yes. xlm-roberta-large is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.