Efficient Inference Infrastructure for Deployable Vision-Language-Action Policies

Deploying VLA policies in real-world robotic systems demands inference latency and throughput far beyond what standard serving stacks provide. We systematically characterize the inference bottlenecks of flow-matching VLA architectures and propose a suite of optimizations including action-chunk caching, speculative denoising, and heterogeneous GPU-CPU pipeline scheduling. Our infrastructure enables sub-20ms end-to-end policy inference on commodity hardware without accuracy degradation, bridging the gap between research models and production robot deployments.