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

arXiv:2607.27205 · cs.CV, cs.RO · Submitted 2026-07-29 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-07-29

Updated: 2026-09-27

Code: https://github.com/H-EmbodVis/TurboVLAhttps:

Project page: https://h-embodvis.github.io/TurboVLA

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Grounding — 8× in this paper · explained in 3 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• VLA Models — 7× in this paper · explained in 3 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Pruning — 4× in this paper · explained in 4 episodes
• Vision-Language-Action (VLA) Models — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Action Chunking — 2× in this paper · explained in 1 episode
• Action Decoder — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
• The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
• CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
• FASTER: Rethinking Real-Time Flow VLAs
• Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
• DINOv3
• StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
• BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation

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