Characterizing VLA Models: Identifying the Action Generation Bottleneck for Edge AI Architectures
Vision-Language-Action (VLA) models are an emerging class of workloads critical for robotics and embodied AI at the edge. As these models scale, they demonstrate significant capability gains, yet they must be deployed locally to meet the strict latency requirements of real-time applications. This paper characterizes VLA performance on two generations of edge hardware, viz. the Nvidia Jetson Orin and Thor platforms. Using MolmoAct-7B, a state-of-the-art VLA model, we identify a primary execution bottleneck: up to 75% of end-to-end latency is consumed by the memory-bound action-generation phase. Through analytical modeling and simulations, we project the hardware requirements for scaling to...
| Authors | Manoj Vishwanathan, Suvinay Subramanian, Anand Raghunathan |
|---|---|
| Primary category | cs.PF |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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