MARK-VLA: Mixed-Precision Action-Risk-Guided Quantization with Mixed-Weight Kernels for Vision-Language-Action Models

Vision-Language-Action (VLA) models couple visual perception, language reasoning, and action generation within a unified policy, but their large model size and computationally intensive inference impose substantial memory and latency overheads. We present MARK-VLA, a post-training quantization framework that enables efficient VLA inference through channel-wise mixed-precision quantization and tailored GPU kernel support for mixed-precision weights. Instead of pursuing extreme compression for individual modules, MARK-VLA applies W2/W4/W8 weight quantization with A8 activation quantization across the major perception-to-action stages to reduce the overall memory footprint. We propose a per-module precision allocation method that automatically determines channel-wise precision based on gradient-based action risk evaluated under a quantized reference policy. To account for the discrepancy between the risk estimated for individual quantization decisions and their combined effect, we adopt a progressive precision reduction scheme that gradually lowers the quantization precision. Finally, to translate these fine-grained precision assignments into practical acceleration, we develop fused mixed-weight GPU kernels together with trace-guided per-layer specialization to optimize the complete execution path of quantized Linear layers. Experiments on LIBERO across six VLA policies and three GPU platforms demonstrate up to $3.62\times$ compression and up to $1.69\times$ end-to-end speedups, while the largest decrease in average success rate is only 0.82 percentage points. These results demonstrate that action-aware precision allocation and deployment-oriented kernel co-design can jointly achieve compact and efficient VLA inference with minimal accuracy degradation.