TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

Hugging Face Daily Papers Papers
vision-language-action real-time robotic-manipulation efficient-inference lightweight-model vla-model

Summary

TurboVLA introduces a new Vision-Language-Action paradigm that directly maps vision and language to action, achieving 97.7% success on LIBERO with only 0.2B parameters and real-time inference at 32 Hz on consumer GPUs, significantly reducing computational cost.

Vision-language-action (VLA) models commonly adopt an LLM-centric V to L to A pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation. In this work, we introduce TurboVLA, a new VLA paradigm that reformulates the conventional V to L to A pathway as a direct V + L to A mapping. Instead of using a large language model as the central interface between perception and action, TurboVLA independently encodes visual observations and language instructions, directly exchanges information between them through lightweight bidirectional vision-language interaction, and predicts continuous action chunks with a compact decoder. This simple design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing the computational and memory costs of VLA inference. On LIBERO, TurboVLA achieves 97.7% average success with only 0.2B parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger VLA policies. These results establish TurboVLA as a simple and effective alternative to the prevailing LLM-centric VLA paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation. Code is available at https://github.com/H-EmbodVis/TurboVLA.
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Paper page - TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

Source: https://huggingface.co/papers/2607.27205

Abstract

Vision-language-action(VLA)modelscommonlyadoptanLLM-centricVtoLtoApathway,wherevisualobservationsareprojectedintotherepresentationspaceofalargelanguagemodelbeforebeingdecodedintorobotactions.Althougheffective,thisdesignincurssubstantialcomputationandmemoryoverheadateverypolicyinvocation.Inthiswork,weintroduceTurboVLA,anewVLAparadigmthatreformulatestheconventionalVtoLtoApathwayasadirectV+LtoAmapping.Insteadofusingalargelanguagemodelasthecentralinterfacebetweenperceptionandaction,TurboVLAindependentlyencodesvisualobservationsandlanguageinstructions,directlyexchangesinformationbetweenthemthroughlightweightbidirectionalvision-languageinteraction,andpredictscontinuousactionchunkswithacompactdecoder.Thissimpledesignconstructstask-conditionedrepresentationsdirectlyfromvisualandlinguisticfeatures,significantlyreducingthecomputationalandmemorycostsofVLAinference.OnLIBERO,TurboVLAachieves97.7%averagesuccesswithonly0.2Bparameters,31.2msinferencelatency,and0.9GBinferenceVRAMonaconsumer-gradeRTX4090,matchingoroutperformingsubstantiallylargerVLApolicies.TheseresultsestablishTurboVLAasasimpleandeffectivealternativetotheprevailingLLM-centricVLAparadigm,offeringanewperspectiveonhowvision,language,andactioncanbeconnectedforefficientroboticmanipulation.Codeisavailableathttps://github.com/H-EmbodVis/TurboVLA.

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