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Current state-of-the-art algorithms force a choice\nbetween either generating accurate mappings at a slow pace, or quickly\ngenerating inaccurate ones, and additionally these methods typically require\nfar too many parameters to be usable on power- or memory-constrained devices.\nMotivated by these shortcomings, we propose a novel approach for disparity\nprediction in the anytime setting. In contrast to prior work, our end-to-end\nlearned approach can trade off computation and accuracy at inference time.\nDepth estimation is performed in stages, during which the model can be queried\nat any time to output its current best estimate. Our final model can process\n1242$ \\times $375 resolution images within a range of 10-35 FPS on an NVIDIA\nJetson TX2 module with only marginal increases in error -- using two orders of\nmagnitude fewer parameters than the most competitive baseline. 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