SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
andito
Abstract
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.
Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive--often with billions of parameters--leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10x larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic benchmarks and release all code, pretrained models, and training data.
Community
Great read. Section 3 is a goldmine of its own.
🥰 thank you so much! 🤗
The paper states that model is trained on 4 GPU, corresponding to 30k gpu hours but it is equivalent as 30k/24/4=312 days. Is the number correct?
I asked the author the same question.
it's project's sum, which accounts for 100+ models trained due to architecture tweaking, hyperparameter tuning, ablations, and ofc testing.
Especially love the async inference contributions here. After trying to run Gr00t on a cloud GPU a few weeks back and experiencing the network latencies significantly impacting performance, I really appreciate the idea of parallelising inference with action execution.
I hope we see other VLAs adopting this architecture, it feels like a key step toward robots sharing cloud GPUs rather than depending on local hardware (reducing marginal cost & increasing maintainability!).
Hey
@willnorris
thank you so much for your words---we're glad you liked the report, and async inference 😉
We're hard at work to make sure the stack lands on main soon. It's already compatible with all the policy types LeRobot supports, and open-sourcing everything is our effort to make this the standard paradigm for the community. Why lagging? 🤓
If you're interested in following progress, check the PR here 🔗 https://github.com/huggingface/lerobot/pull/1196
Hey
@willnorris
thank you so much for your words---we're glad you liked the report, and async inference 😉
We're hard at work to make sure the stack lands on main soon. It's already compatible with all the policy types LeRobot supports, and open-sourcing everything is our effort to make this the standard paradigm for the community. Why lagging? 🤓
If you're interested in following progress, check the PR here 🔗 https://github.com/huggingface/lerobot/pull/1196
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