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Jim Fan Introduces VFlash for Faster Physical AI Inference

1 reports 1 reporting sources Updated 39 minutes ago

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Jim Fan introduced VFlash, a model that predicts fixed-length geometric outputs such as object locations and robot trajectories in parallel, and said its paper and source code are open. He reports up to 12× faster action decoding and a 5.1× overall robot inference speedup on edge GPUs, including Jetson Thor and DGX Spark. Fan also reports up to 61% lower 3D trajectory error than the vision-language model backbone. These are reported results for VFlash; the figures describe different measures of speed and accuracy.

Generated by AI from reports · Updated 18 minutes ago

Latest development11/6 00:31 (UTC+8)
Jim Fan Introduces VFlash for Faster Physical AI InferenceJim Fan · X

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11/6
  1. Jim Fan · X
    Jim Fan Introduces VFlash for Faster Physical AI Inference

    Jim Fan introduces VFlash, which outputs fixed-length geometric predictions such as object locations and robot trajectories in parallel rather than one token at a time. He reports up to 12x faster action decoding, a 5.1x overall robot inference speedup on edge GPUs including Jetson Thor and DGX Spark, and up to 61% lower 3D trajectory error than the VLM backbone. Fan says the paper and source code are open.

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