all-MiniLM-L6-v2 by Sentence Transformers: Open-Weight Model

Runs On

What it takes to serve all-MiniLM-L6-v2 (23M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	0.1 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x 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, read Sep 18, 2026.

Model Card

By Sentence Transformers, published under apache-2.0, revision 1110a243fdf4.

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 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-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

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.

Read the full model card (760 words)

Configuration

Architecture: BertModel
Context length (tokens): 512
Layers: 6
Hidden size: 384
Feed-forward size: 1,536
Attention heads: 12
Vocabulary size: 30,522
Model type: bert

Identity and Version

Repository: sentence-transformers/all-MiniLM-L6-v2
Publisher: Sentence Transformers
Task: Sentence similarity
Modality: Text
Library: sentence-transformers
Parameters: 23M parameters
Languages: en
Revision: 1110a243fdf4706b3f48f1d95db1a4f5529b4d41
First published: 2022-03-02
Last updated: 2026-06-01

Files and Weights

30 files, 976.9 MB in total. The weights are 15 files totalling 975.6 MB in bin, h5, onnx, ot, safetensors.

Weights15 files · 975.6 MB

Configuration8 files · 53.9 KB

Tokenizer3 files · 698.1 KB

Documentation1 file · 10.5 KB

Other2 files · 579.3 KB

Repository1 file · 1.2 KB

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	90.9 MB	53aa51172d14
onnx/model.onnx	Weights	90.4 MB	6fd5d72fe458
onnx/model_O1.onnx	Weights	90.4 MB	1391c6fc20b5
onnx/model_O2.onnx	Weights	90.3 MB	1de390502919
onnx/model_O3.onnx	Weights	90.3 MB	a44f671e364d
onnx/model_O4.onnx	Weights	45.2 MB	1667d7f3ba66
onnx/model_qint8_arm64.onnx	Weights	23.0 MB	4278337fd0ff
onnx/model_qint8_avx512.onnx	Weights	23.0 MB	4278337fd0ff
onnx/model_qint8_avx512_vnni.onnx	Weights	23.0 MB	4278337fd0ff
onnx/model_quint8_avx2.onnx	Weights	23.0 MB	b941bf19f1f1
openvino/openvino_model.bin	Weights	90.3 MB	8b86cab4722e
openvino/openvino_model_qint8_quantized.bin	Weights	22.9 MB	c92ea4af3c6b
pytorch_model.bin	Weights	90.9 MB	c3a85f238711
rust_model.ot	Weights	90.9 MB	2d98d96d2783
tf_model.h5	Weights	91.0 MB	24c06a7429b8
1_Pooling/config.json	Configuration	190 B	—
config.json	Configuration	612 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	112 B	—
train_script.py	Configuration	13.2 KB	—
README.md	Documentation	10.5 KB	—
openvino/openvino_model.xml	Other	211.3 KB	—
openvino/openvino_model_qint8_quantized.xml	Other	368.0 KB	—
.gitattributes	Repository	1.2 KB	—
tokenizer.json	Tokenizer	466.2 KB	—
tokenizer_config.json	Tokenizer	350 B	—
vocab.txt	Tokenizer	231.5 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 975.6 MB

Download from Sentence Transformers

Released by Sentence Transformers through its official repository on Hugging Face. Read the license.

Built From

Derived from nreimers/MiniLM-L6-H384-uncased
Described by arXiv:1704.05179
Described by arXiv:1810.09305
Described by arXiv:1904.06472
Described by arXiv:2102.07033
Described by arXiv:2104.08727
Quantized from nreimers/MiniLM-L6-H384-uncased
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/arguana	Task ArguAnaMetric ArguAnaSetup Obtained using MTEB v1.12.75Comparison conditions not established	50.167	Obtained using MTEB v1.12.75 Reported by a third party	Evaluated revision not stated	2026-03-05
mteb/arguana	Task ArguAna_default_testMetric ArguAna_default_testSetup Obtained using MTEB v1.12.75Comparison conditions not established	50.167	Obtained using MTEB v1.12.75 Reported by a third party	Evaluated revision not stated	2026-03-05

Memory Requirements

Precision	Weights in memory
As published	975.6 MB
16-bit	0.0 GB
8-bit	0.0 GB
4-bit	0.0 GB

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

Built on This Model

Quantized fromall-MiniLM-L6-v2
Derived fromall-MiniLM-L6-v2

Compare all-MiniLM-L6-v2

Questions About all-MiniLM-L6-v2

How much GPU memory does all-MiniLM-L6-v2 need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (23M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run all-MiniLM-L6-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-MiniLM-L6-v2 commercially?

Yes. all-MiniLM-L6-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-MiniLM-L6-v2's context length?

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

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