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FoldQuantVLA introduces a post-training quantization framework that enables four-bit execution of vision-language-action models on edge hardware like Jetson AGX Orin without retraining policies. By combining channel scaling with dynamic per-token quantization, the method achieves significant latency reductions while maintaining high success rates in real-robot tasks compared to uniform low-bit approaches. This work directly addresses the critical need for efficient, low-latency inference in embodied AI systems operating on resource-constrained devices.
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
arXiv:2609.24433v1 Announce Type: cross Abstract: Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-training quantization framework that carries a consistent activation representation through calibration, weight rounding, and native integer execution. It combines channel scaling and block Hadamard transforms with dynamic per-token quantization, without policy retraining. Custom TensorRT plugins execute projections in both the language backbone and iterative action expert with four-bit weights and activations (W4A4) on Ada GPUs and Jetson AGX Orin. Evaluation spans LIBERO, SimplerEnv, and two robot platforms. Across three GR00T checkpoints and $\pi_{0.5}$, W4A4 achieves $1.20$ to $1.33\times$ speedups over floating-point TensorRT on Orin and $1.25$ to $1.52\times$ on desktop. Retaining language attention-output and feed-forward down projections at eight bits (W8A8) improves held-out action fidelity on all four checkpoints. Across four real-robot tasks, this configuration raises observed GR00T N1.7 success from $80.0\%$ with uniform W4A4 to $92.5\%$ over 80 trials per configuration, with a measured additional Orin latency of 1 ms.
Abstract:Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-training quantization framework that carries a consistent activation representation through calibration, weight rounding, and native integer execution. It combines channel scaling and block Hadamard transforms with dynamic per-token quantization, without policy retraining. Custom TensorRT plugins execute projections in both the language backbone and iterative action expert with four-bit weights and activations (W4A4) on Ada GPUs and Jetson AGX Orin. Evaluation spans LIBERO, SimplerEnv, and two robot platforms. Across three GR00T checkpoints and $\pi_{0.5}$, W4A4 achieves $1.20$ to $1.33\times$ speedups over floating-point TensorRT on Orin and $1.25$ to $1.52\times$ on desktop. Retaining language attention-output and feed-forward down projections at eight bits (W8A8) improves held-out action fidelity on all four checkpoints. Across four real-robot tasks, this configuration raises observed GR00T N1.7 success from $80.0\%$ with uniform W4A4 to $92.5\%$ over 80 trials per configuration, with a measured additional Orin latency of 1 ms.
8 pages, 5 figures, 7 tables. Code: this https URL
Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
arXiv:2609.24433 [cs.RO]
(or arXiv:2609.24433v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.24433 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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