A new paper on arXiv, titled "FlashRT-PAI: Fast Kernels Are Not Fast Pipelines for Physical AI Inference," challenges the common assumption that optimizing compute kernels is enough to speed up physical AI systems. The authors focus on vision-language-action (VLA), video-action (VA), and world action models (WAMs), which are central to modern robotics and embodied AI.
The paper notes that real-time single-batch inference remains a defining bottleneck for these models. While the abstract is cut off, the title makes the central claim clear: even when individual kernels are made fast, the overall pipeline can still be slow. This suggests that latency is dominated by other factors—such as data movement, scheduling, or inter-kernel overhead—rather than raw compute.
The implication is that researchers and engineers should look beyond kernel micro-optimizations and consider end-to-end pipeline design. FlashRT-PAI appears to propose a framework or set of techniques aimed at addressing this gap, though the full details are not available in the abstract alone.