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Open-weight model · Robotics

openvla-7b

by OpenVLA Collaboration openvla/openvla-7b

OpenVLA 7B (openvla-7b) is an open vision-language-action model trained on 970K robot manipulation episodes from the Open X-Embodiment dataset. The model takes language instructions and camera images as input and generates robot actions.

Parameters7.5B
Context
Weights15.1 GB
Licensemit
AccessOpen weights
Monthly Downloads444.8k

Runs On

What it takes to serve openvla-7b (7.5B 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 15.1 GB 18.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 7.5 GB 9.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 3.8 GB 4.5 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 OpenVLA Collaboration, published under mit, revision 47a0ec7fc4ec.

OpenVLA 7B (openvla-7b) is an open vision-language-action model trained on 970K robot manipulation episodes from the Open X-Embodiment dataset. The model takes language instructions and camera images as input and generates robot actions. It supports controlling multiple robots out-of-the-box, and can be quickly adapted for new robot domains via (parameter-efficient) fine-tuning.

All OpenVLA checkpoints, as well as our training codebase are released under an MIT License.

For full details, please read our paper and see our project page.

Model Summary

Read the full model card (536 words)

Configuration

Architecture
OpenVLAForActionPrediction
Vocabulary size
32,064
Stored precision
bfloat16
Model type
openvla

Identity and Version

Repository
openvla/openvla-7b
Publisher
OpenVLA Collaboration
Task
Robotics
Modality
Control
Library
transformers
Parameters
7.5B parameters
Languages
en
Revision
47a0ec7fc4ec123775a391911046cf33cf9ed83f
First published
2024-06-10
Last updated
2026-02-17

Files and Weights

18 files, 15.1 GB in total. The weights are 3 files totalling 15.1 GB in safetensors.

Weights3 files · 15.1 GB
Configuration10 files · 202.6 KB
Tokenizer3 files · 2.3 MB
Documentation1 file · 5.0 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00003.safetensorsWeights6.9 GB 10d863625601
model-00002-of-00003.safetensorsWeights7.0 GB 2050b14f21d4
model-00003-of-00003.safetensorsWeights1.2 GB ea65305a1577
added_tokens.jsonConfiguration21 B
config.jsonConfiguration60.7 KB
configuration_prismatic.pyConfiguration5.9 KB
generation_config.jsonConfiguration136 B
model.safetensors.index.jsonConfiguration94.8 KB
modeling_prismatic.pyConfiguration26.1 KB
preprocessor_config.jsonConfiguration1.6 KB
processing_prismatic.pyConfiguration12.7 KB
processor_config.jsonConfiguration130 B
special_tokens_map.jsonConfiguration552 B
README.mdDocumentation5.0 KB
.gitattributesRepository1.5 KB
tokenizer.jsonTokenizer1.8 MB
tokenizer.modelTokenizer499.7 KB 9e556afd4421
tokenizer_config.jsonTokenizer1.2 KB

License and Download

License
mit
Access
Open weights, no gate
Download size
15.1 GB
Download from OpenVLA Collaboration

Released by OpenVLA Collaboration through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published15.1 GB
16-bit15.1 GB
8-bit7.5 GB
4-bit3.8 GB

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

Questions About openvla-7b

How much GPU memory does openvla-7b need?

About 18.1 GB at 16-bit and 4.5 GB at 4-bit: the weights (7.5B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run openvla-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 openvla-7b commercially?

Yes. openvla-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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This repository contains the OpenVLA-OFT checkpoint for LIBERO-Spatial, as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?searchmodels=oft This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.

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