SmolVLA: A Vision-Language-Action Model for Affordable and Efficient
  Robotics
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.
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Mustafa Shukor
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2025-06-03T09:11:09.058Z
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Mustafa Shukor
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Dana Aubakirova
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2025-06-03T09:11:18.586Z
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Dana Aubakirova
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Francesco Capuano
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2025-06-03T09:11:29.639Z
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Francesco Capuano
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Pepijn Kooijmans
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2025-06-03T09:11:38.693Z
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Pepijn Kooijmans
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Steven Palma
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2025-06-03T09:11:47.379Z
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Steven Palma
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Adil Zouitine
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2025-06-03T09:11:56.957Z
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Adil Zouitine
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Michel Aractingi
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2025-06-03T09:12:05.798Z
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Michel Aractingi
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Caroline Pascal
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2025-06-03T09:12:15.340Z
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Caroline Pascal
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Martino Russi
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2025-06-03T09:12:23.474Z
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Martino Russi
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Andres Marafioti
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2025-06-03T09:12:31.740Z
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Andres Marafioti
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Simon Alibert
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2025-06-03T09:12:39.999Z
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Simon  Alibert
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Matthieu Cord
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Thomas Wolf
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2025-06-03T09:13:02.139Z
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Thomas Wolf
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Remi Cadene
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2025-06-03T09:13:11.882Z
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Remi Cadene
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cadene
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient
  Robotics
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.
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 multimodal datasets robotic policies vision-language-action models natural language-driven perception asynchronous inference action prediction action execution chunked action generation performance benchmarks