SAVRN Model Hub · Comparisons
bert-large-uncased vs xlm-roberta-base
Bert-large-uncased has 336M parameters and xlm-roberta-base has 279M parameters; bert-large-uncased is released under Apache License 2.0 and xlm-roberta-base under MIT License; at 16-bit, bert-large-uncased needs about 0.8 GB (1x MI300X from $1.85 an hour) and xlm-roberta-base about 0.7 GB (1x MI300X from $1.85 an hour).
| Field | bert-large-uncased google-bert/bert-large-uncased | xlm-roberta-base FacebookAI/xlm-roberta-base |
|---|---|---|
| Publisher | BERT community | Facebook AI community |
| Task | Fill mask | Fill mask |
| Modality | Text | Text |
| Parameters, as reported | 336M parameters | 279M parameters |
| Architecture | BertForMaskedLM | XLMRobertaForMaskedLM |
| Library | transformers | transformers |
| Context length | 512 tokens | 514 tokens |
| Repository size | 8.2 GB | 6.4 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 | 0.7 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 | 0.2 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 6da4b6a26a18 | e73636d4f797 |
| Downloads reported by the hub | 1M | 21.5M |
| Last observed | 2026-09-18 | 2026-09-19 |
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-base
Fill-mask work across 100 languages is where this model earns its place, and the memory math barely registers. At 16-bit the weights are 0.6 GB and the run needs 0.7 GB; the cheapest setup in our table is one MI300X with 192 GB at $1.85 an hour on-demand, leaving nearly all of that card idle. At 8-bit the need is 0.3 GB. With 279M parameters over 12 layers, it fits anywhere a few hundred megabytes of accelerator memory are free.
MIT terms permit commercial use, modification and redistribution; the one obligation is keeping the copyright and permission notices with the files. Check two things before committing: the 514-token context caps each input, and the 6.35 GB download spans 11 files in five formats, so you need only a fraction of it. The page lists no reported evaluations and no host prices, and the last update was February 2024.
Questions
Which is larger, bert-large-uncased or xlm-roberta-base?
bert-large-uncased (336M parameters) is larger than xlm-roberta-base (279M parameters), by the parameter counts their publishers report.
Which is cheaper to run, bert-large-uncased or xlm-roberta-base?
At 4-bit, bert-large-uncased fits on 1x MI300X from $1.85 an hour and xlm-roberta-base 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-base commercially?
Yes. xlm-roberta-base 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.