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SmolVLA is a compact 450M open-source Vision-Language-Action model from Hugging Face, pretrained on community-shared LeRobot datasets.

Documentation in progress

The product identity and source are confirmed, but some specifications or interface details do not yet have citable manufacturer evidence. Missing evidence is not treated as a negative claim.

Product information

Information below comes from the product website or public documentation. Pricing and availability may change.

Category
Robot Development
Maturityi
Research project
Deploymenti
Not specified
Source
Official websiteChecked Sep 18, 2026

Reality check

A demo shows what may be possible. These points help judge whether the product fits day-to-day use.

Works well when
  • Simulation, control, perception and repeatable experiments
  • Teams that can manage dependencies and hardware interfaces
  • Prototyping before committing to a physical deployment
What to check
  • Supported hardware, GPU requirements and version compatibility
  • Documentation, examples and community response
  • How simulation results transfer to the real robot
Not a magic solution
  • A guarantee that simulation equals a production cell
  • A replacement for safety validation on hardware
  • A low-maintenance toolchain without version ownership

About

SmolVLA is a 450M-parameter open-source Vision-Language-Action model released by Hugging Face on June 3, 2025. It combines a SmolVLM2 vision-language backbone with a flow-matching action expert, and is pretrained only on compatibly licensed community datasets under the lerobot tag. The model runs on consumer hardware, including CPU and a single GPU, and supports asynchronous inference for faster response and higher task throughput. It is evaluated on simulation benchmarks such as LIBERO and Meta-World and on real-world SO100 and SO101 tasks.

Scenes

Product details

Manufacturer / team
Hugging Face
Product typei
Framework
Open source

The cost iceberg

The purchase price is only one part of an automation project.

GPU, simulation assets and hardware adapters
Dependencies, datasets and test infrastructure
Version pinning, documentation and support
Sim-to-real gaps and safety validation