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Open-weight model · Image and text to text

blip2-opt-2.7b

by Salesforce AI Research Salesforce/blip2-opt-2.7b

BLIP-2 model, leveraging OPT-2.7b (a large language model with 2.7 billion parameters). It was introduced in the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Li et al.

Parameters3.7B
Context—
Weights30.5 GB
Licensemit
AccessOpen weights
Monthly Downloads662.5k

Runs On

What it takes to serve blip2-opt-2.7b (3.7B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 7.5 GB 9.0 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 3.7 GB 4.5 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 1.9 GB 2.2 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.

SAVRN's Notes on blip2-opt-2.7b

Nine gigabytes of memory is the number to plan around, which is what the 16-bit run needs with 7.5 GB of weights loaded. That puts captioning and image question answering on one 192 GB MI300X at $1.85 per hour on demand, the cheapest setup we track, with most of the card still free. Mind the gap between that figure and the download: the files total 30.5 GB because the weights are stored in float32, so size storage to the larger number.

MIT is about as light as a license gets. Commercial use, modification and redistribution are permitted, and the only obligation is keeping the copyright and permission notices with the files. Two checks: the release date is 2023-02-06 and the last update 2025-02-03, so weigh it against newer image-and-text models, and our file lists no context length, so test your own prompt and image sizes.

SAVRN Research, 2026-09-18

Model Card

By Salesforce AI Research, published under mit, revision 59a1ef6c1e51.

BLIP-2, OPT-2.7b, pre-trained only

BLIP-2 model, leveraging OPT-2.7b (a large language model with 2.7 billion parameters). It was introduced in the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Li et al. and first released in this repository.

Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.

The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model.

Read the full model card (812 words)

Configuration

Architecture
Blip2ForConditionalGeneration
Layers
32
Hidden size
2,560
Attention heads
32
Vocabulary size
50,304
Stored precision
float32
Model type
blip-2

Identity and Version

Repository
Salesforce/blip2-opt-2.7b
Publisher
Salesforce AI Research
Task
Image and text to text
Modality
Image and text
Library
transformers
Parameters
3.7B parameters
Languages
en
Revision
59a1ef6c1e5117b3f65523d1c6066825bcf315e3
First published
2023-02-06
Last updated
2025-02-03

Files and Weights

18 files, 30.5 GB in total. The weights are 4 files totalling 30.5 GB in bin, safetensors.

Weights4 files · 30.5 GB
Configuration8 files · 245.6 KB
Tokenizer4 files · 4.8 MB
Documentation1 file · 8.1 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00002.safetensorsWeights10.0 GB b81228c9ac1b
model-00002-of-00002.safetensorsWeights5.0 GB 536bd73b8f1d
pytorch_model-00001-of-00002.binWeights10.0 GB 83f4604e9f2c
pytorch_model-00002-of-00002.binWeights5.5 GB b224ac0c148b
added_tokens.jsonConfiguration23 B —
config.jsonConfiguration1.0 KB —
generation_config.jsonConfiguration141 B —
model.safetensors.index.jsonConfiguration121.7 KB —
preprocessor_config.jsonConfiguration432 B —
processor_config.jsonConfiguration68 B —
pytorch_model.bin.index.jsonConfiguration121.7 KB —
special_tokens_map.jsonConfiguration548 B —
README.mdDocumentation8.1 KB —
.gitattributesRepository1.5 KB —
merges.txtTokenizer456.3 KB —
tokenizer.jsonTokenizer3.6 MB —
tokenizer_config.jsonTokenizer882 B —
vocab.jsonTokenizer798.3 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
30.5 GB
Download from Salesforce AI Research

Released by Salesforce AI Research through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published30.5 GB
16-bit7.5 GB
8-bit3.7 GB
4-bit1.9 GB

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

Compare blip2-opt-2.7b

Questions About blip2-opt-2.7b

How much GPU memory does blip2-opt-2.7b need?

About 9 GB at 16-bit and 2.2 GB at 4-bit: the weights (3.7B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run blip2-opt-2.7b 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 blip2-opt-2.7b commercially?

Yes. blip2-opt-2.7b 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.

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