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Open-weight model · Robotics

smolvla_libero

by Hugging Face Vision Language Action Models Research HuggingFaceVLA/smolvla_libero

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot.

Parameters605M
Context—
Weights1.2 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads28.8k

Runs On

What it takes to serve smolvla_libero (605M 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 1.2 GB 1.5 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.6 GB 0.7 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.3 GB 0.4 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 Hugging Face Vision Language Action Models Research, published under apache-2.0, revision 6721902bc4d6.

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Read Hugging Face Vision Language Action Models Research's full model card

Model Card for smolvla

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.

How to Get Started with the Model

For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:

Train from scratch

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true

Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.

Evaluate the policy/run inference

lerobot-record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.

Model Details

  • License: apache-2.0

Identity and Version

Repository
HuggingFaceVLA/smolvla_libero
Publisher
Hugging Face Vision Language Action Models Research
Task
Robotics
Modality
Control
Library
lerobot
Parameters
605M parameters
Languages
Not stated by the source
Revision
6721902bc4d61e50a3bfdb11dfb4cb626f05d102
First published
2025-09-17
Last updated
2025-09-17

Files and Weights

8 files, 1.2 GB in total. The weights are 3 files totalling 1.2 GB in safetensors.

Weights3 files · 1.2 GB
Configuration3 files · 4.5 KB
Documentation1 file · 1.7 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights1.2 GB 71d9563c8295
policy_postprocessor_step_1_unnormalizer_processor.safetensorsWeights416 B 7008ba73c738
policy_preprocessor_step_5_normalizer_processor.safetensorsWeights416 B 7008ba73c738
config.jsonConfiguration2.1 KB —
policy_postprocessor.jsonConfiguration660 B —
policy_preprocessor.jsonConfiguration1.7 KB —
README.mdDocumentation1.7 KB —
.gitattributesRepository1.5 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
1.2 GB
Download from Hugging Face Vision Language Action Models Research

Released by Hugging Face Vision Language Action Models Research through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published1.2 GB
16-bit1.2 GB
8-bit0.6 GB
4-bit0.3 GB

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

Questions About smolvla_libero

How much GPU memory does smolvla_libero need?

About 1.5 GB at 16-bit and 0.4 GB at 4-bit: the weights (605M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run smolvla_libero 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 smolvla_libero commercially?

Yes. smolvla_libero 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.

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