# all-mpnet-base-v2 by Sentence Transformers: Open Model
Source: https://savrn.com/models/all-mpnet-base-v2
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Runs On

What it takes to serve all-mpnet-base-v2 (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
| --- | --- | --- | --- | --- | --- |
| 16-bit | 0.2 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026.

[all-mpnet-base-v2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/all-mpnet-base-v2/gpus)

## SAVRN's Notes on all-mpnet-base-v2

Semantic search and clustering start with an embedding model, and this one from Sentence Transformers turns each sentence or paragraph into a 768-dimension vector. At 109M parameters it needs 0.2 GB of weights at 16-bit and 0.3 GB of memory, so the cheapest setup we price, one MI300X with 192 GB at $1.85 per hour on-demand, leaves nearly the whole card empty. Run it beside a generation model, or batch a document store through it and release the hardware.

Under Apache 2.0 you may use it commercially, modify it and redistribute it, provided the license and copyright notices stay and significant changes are stated, so a private deployment raises no license question. Check the 514-token context; anything longer must be chunked. Look at the eight training sets the page names, from s2orc and ms_marco to code_search_net and eli5, against your own corpus.

SAVRN Research, 2026-09-18

## Model Card

By Sentence Transformers, published under apache-2.0, revision e8c3b32edf54.

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

### Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```

### Usage (HuggingFace Transformers)

Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

[Read the full model card (859 words)](https://savrn.com/models/all-mpnet-base-v2/card)

## Configuration

Architecture

MPNetForMaskedLM

Context length (tokens)

514

Layers

12

Hidden size

768

Feed-forward size

3,072

Attention heads

12

Vocabulary size

30,527

Model type

mpnet

## Identity and Version

Repository

sentence-transformers/all-mpnet-base-v2

Publisher

Sentence Transformers

Task

Sentence similarity

Modality

Text

Library

sentence-transformers

Parameters

109M parameters

Languages

en

Revision

e8c3b32edf5434bc2275fc9bab85f82640a19130

First published

2022-03-02

Last updated

2025-08-19

## Files and Weights

28 files, 3.8 GB in total. The weights are 13 files totalling 3.8 GB in bin, onnx, safetensors.

Weights13 files · 3.8 GB

Configuration8 files · 53.9 KB

Tokenizer3 files · 697.9 KB

Documentation1 file · 11.6 KB

Other2 files · 1.2 MB

Repository1 file · 1.2 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model.safetensors | Weights | 438.0 MB | 78c0197b6159 |
| onnx/model.onnx | Weights | 435.8 MB | 74187b16d9c9 |
| onnx/model_O1.onnx | Weights | 435.7 MB | 5c0b47004076 |
| onnx/model_O2.onnx | Weights | 435.7 MB | 14d01256f5f3 |
| onnx/model_O3.onnx | Weights | 435.7 MB | dd5551070603 |
| onnx/model_O4.onnx | Weights | 217.9 MB | cab2a54139fc |
| onnx/model_qint8_arm64.onnx | Weights | 110.1 MB | c392a9c545c7 |
| onnx/model_qint8_avx512.onnx | Weights | 110.1 MB | c392a9c545c7 |
| onnx/model_qint8_avx512_vnni.onnx | Weights | 110.1 MB | c392a9c545c7 |
| onnx/model_quint8_avx2.onnx | Weights | 110.2 MB | aa5c27172d77 |
| openvino/openvino_model.bin | Weights | 435.6 MB | 5c3279d83388 |
| openvino/openvino_model_qint8_quantized.bin | Weights | 110.0 MB | fde0c650018f |
| pytorch_model.bin | Weights | 438.0 MB | a8fd120b1a00 |
| 1_Pooling/config.json | Configuration | 190 B | — |
| config.json | Configuration | 571 B | — |
| config_sentence_transformers.json | Configuration | 116 B | — |
| data_config.json | Configuration | 39.3 KB | — |
| modules.json | Configuration | 349 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 239 B | — |
| train_script.py | Configuration | 13.1 KB | — |
| README.md | Documentation | 11.6 KB | — |
| openvino/openvino_model.xml | Other | 432.8 KB | — |
| openvino/openvino_model_qint8_quantized.xml | Other | 741.9 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 466.0 KB | — |
| tokenizer_config.json | Tokenizer | 363 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |

## License and Download

License

apache-2.0

Access

Open weights, no gate

Download size

3.8 GB

[Download from Sentence Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)

Released by Sentence Transformers through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0).

## Built From

- Described by [arXiv:1704.05179](https://savrn.com/papers/searchqa-a-new-q-a-dataset-augmented-with-context-from-a-search-engine)
- Described by [arXiv:1810.09305](https://savrn.com/papers/wikihow-a-large-scale-text-summarization-dataset)
- Described by [arXiv:1904.06472](https://savrn.com/papers/a-repository-of-conversational-datasets)
- Described by [arXiv:2102.07033](https://savrn.com/papers/paq-65-million-probably-asked-questions-and-what-you-can-do-with-them)
- Described by [arXiv:2104.08727](https://savrn.com/papers/gooaq-open-question-answering-with-diverse-answer-types)
- Trained on (disclosed) code_search_net
- Trained on (disclosed) eli5
- Trained on (disclosed) embedding-data/PAQ_pairs
- Trained on (disclosed) embedding-data/QQP
- Trained on (disclosed) embedding-data/SPECTER
- Trained on (disclosed) embedding-data/WikiAnswers
- Trained on (disclosed) embedding-data/altlex
- Trained on (disclosed) embedding-data/flickr30k-captions
- Trained on (disclosed) embedding-data/sentence-compression
- Trained on (disclosed) embedding-data/simple-wiki
- Trained on (disclosed) flax-sentence-embeddings/stackexchange_xml
- Trained on (disclosed) gooaq
- Trained on (disclosed) ms_marco
- Trained on (disclosed) multi_nli
- Trained on (disclosed) natural_questions
- Trained on (disclosed) s2orc
- Trained on (disclosed) search_qa
- Trained on (disclosed) snli
- Trained on (disclosed) trivia_qa
- Trained on (disclosed) wikihow
- Trained on (disclosed) yahoo_answers_topics

## Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

| Benchmark | Conditions | Result | Reported by | Revision | Date |
| --- | --- | --- | --- | --- | --- |
| mteb/BRIGHT | Task BrightAopsRetrievalMetric BrightAopsRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 5.325 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightAopsRetrieval_default_standardMetric BrightAopsRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 5.325 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightBiologyLongRetrievalMetric BrightBiologyLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 25.566 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightBiologyLongRetrieval_default_longMetric BrightBiologyLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 25.566 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightBiologyRetrievalMetric BrightBiologyRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 15.098 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightBiologyRetrieval_default_standardMetric BrightBiologyRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 15.098 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEarthScienceLongRetrievalMetric BrightEarthScienceLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 34.052 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEarthScienceLongRetrieval_default_longMetric BrightEarthScienceLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 34.052 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEarthScienceRetrievalMetric BrightEarthScienceRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 20.414 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEarthScienceRetrieval_default_standardMetric BrightEarthScienceRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 20.414 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEconomicsLongRetrievalMetric BrightEconomicsLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 18.932 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEconomicsLongRetrieval_default_longMetric BrightEconomicsLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 18.932 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEconomicsRetrievalMetric BrightEconomicsRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 16.639 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightEconomicsRetrieval_default_standardMetric BrightEconomicsRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 16.639 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightLeetcodeRetrievalMetric BrightLeetcodeRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 26.404 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightLeetcodeRetrieval_default_standardMetric BrightLeetcodeRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 26.404 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPonyLongRetrievalMetric BrightPonyLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 1.19 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPonyLongRetrieval_default_longMetric BrightPonyLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 1.19 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPonyRetrievalMetric BrightPonyRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 6.996 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPonyRetrieval_default_standardMetric BrightPonyRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 6.996 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPsychologyLongRetrievalMetric BrightPsychologyLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 15.842 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPsychologyLongRetrieval_default_longMetric BrightPsychologyLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 15.842 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPsychologyRetrievalMetric BrightPsychologyRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 22.664 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightPsychologyRetrieval_default_standardMetric BrightPsychologyRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 22.664 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightRoboticsLongRetrievalMetric BrightRoboticsLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 10.891 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightRoboticsLongRetrieval_default_longMetric BrightRoboticsLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 10.891 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightRoboticsRetrievalMetric BrightRoboticsRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 8.221 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightRoboticsRetrieval_default_standardMetric BrightRoboticsRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 8.221 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightStackoverflowLongRetrievalMetric BrightStackoverflowLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 14.957 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightStackoverflowLongRetrieval_default_longMetric BrightStackoverflowLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 14.957 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightStackoverflowRetrievalMetric BrightStackoverflowRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 11.024 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightStackoverflowRetrieval_default_standardMetric BrightStackoverflowRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 11.024 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightSustainableLivingLongRetrievalMetric BrightSustainableLivingLongRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 18.009 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightSustainableLivingLongRetrieval_default_longMetric BrightSustainableLivingLongRetrieval_default_longSetup Obtained using MTEB v2.10.12Comparison conditions not established | 18.009 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightSustainableLivingRetrievalMetric BrightSustainableLivingRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 15.343 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightSustainableLivingRetrieval_default_standardMetric BrightSustainableLivingRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 15.343 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightTheoremQAQuestionsRetrievalMetric BrightTheoremQAQuestionsRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 20.035 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightTheoremQAQuestionsRetrieval_default_standardMetric BrightTheoremQAQuestionsRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 20.035 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightTheoremQATheoremsRetrievalMetric BrightTheoremQATheoremsRetrievalSetup Obtained using MTEB v2.10.12Comparison conditions not established | 10.779 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |
| mteb/BRIGHT | Task BrightTheoremQATheoremsRetrieval_default_standardMetric BrightTheoremQATheoremsRetrieval_default_standardSetup Obtained using MTEB v2.10.12Comparison conditions not established | 10.779 | [Obtained using MTEB v2.10.12](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-26 |

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 3.8 GB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Compare all-mpnet-base-v2

- [all-mpnet-base-v2 vs multi-qa-mpnet-base-dot-v1](https://savrn.com/models/compare/all-mpnet-base-v2-vs-multi-qa-mpnet-base-dot-v1)
- [all-mpnet-base-v2 vs paraphrase-mpnet-base-v2](https://savrn.com/models/compare/all-mpnet-base-v2-vs-paraphrase-mpnet-base-v2)
- [all-mpnet-base-v2 vs paraphrase-multilingual-MiniLM-L12-v2](https://savrn.com/models/compare/all-mpnet-base-v2-vs-paraphrase-multilingual-minilm-l12-v2)

## Questions About all-mpnet-base-v2

### How much GPU memory does all-mpnet-base-v2 need?

About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run all-mpnet-base-v2 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use all-mpnet-base-v2 commercially?

Yes. all-mpnet-base-v2 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.

### What is all-mpnet-base-v2's context length?

514 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Sentence similarity

### [multi-qa-mpnet-base-dot-v1](https://savrn.com/models/multi-qa-mpnet-base-dot-v1)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. Text Embeddings Inference (TEI) is a blazing fast…

Open weights 109M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/multi-qa-mpnet-base-dot-v1)

Model · Sentence similarity

### [paraphrase-mpnet-base-v2](https://savrn.com/models/paraphrase-mpnet-base-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Text Embeddings Inference (TEI) is a blazing fast inference solution for text embedding models. Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API: Or check…

Open weights apache-2.0 109M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/paraphrase-mpnet-base-v2)

Model · Sentence similarity

### [nli-mpnet-base-v2](https://savrn.com/models/nli-mpnet-base-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Text Embeddings Inference (TEI) is a blazing fast inference solution for text embedding models. Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API: Or check…

Open weights apache-2.0 109M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/nli-mpnet-base-v2)

Model · Sentence similarity

### [lt-wikidata-comp-en](https://savrn.com/models/lt-wikidata-comp-en)

[Dell Research Harvard](https://savrn.com/model-publishers/dell-research-harvard)

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…

Open weights 109M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/lt-wikidata-comp-en)

Model · Sentence similarity

### [lt-un-data-fine-fine-en](https://savrn.com/models/lt-un-data-fine-fine-en)

[Dell Research Harvard](https://savrn.com/model-publishers/dell-research-harvard)

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…

Open weights 109M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/lt-un-data-fine-fine-en)

Model · Sentence similarity

### [names-linking-model](https://savrn.com/models/names-linking-model)

[Dell Research Harvard](https://savrn.com/model-publishers/dell-research-harvard)

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…

Open weights 109M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/names-linking-model)

## Sentence Transformers

[All models and datasets](https://savrn.com/model-publishers/sentence-transformers)

## Versions

- [e8c3b32edf54](https://savrn.com/models/all-mpnet-base-v2/versions/e8c3b32edf54) · current 2026-10-25

## In Collections

- [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval)

## Explore More

- [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity)
- [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0)
- [Model comparisons](https://savrn.com/models/comparisons)
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## Source

- Repository metadata, read 2026-10-19.
- [Sentence Transformers website](https://sbert.net/)
- [Hugging Face record](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- [How the hub is built](https://savrn.com/model-hub/methodology)