NVIDIA等提出物理AI视觉规划解码框架VFlash
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2026年11月7日，NVIDIA联合多家高校推出了针对物理AI的视觉规划解码框架VFlash。该框架通过利用几何结构先验，能够并行生成完整轨迹与视觉基元，从而摆脱传统自回归逐token生成的延迟瓶颈。性能测试显示，在Jetson Thor平台上，VFlash将解码阶段提速12至42倍，使整体机器人模型流程提速5.1倍；同时在自动驾驶与机械臂任务中，将平均3D位移误差降低了42.6%。
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- Hacker News · AISelectedNVIDIA and Partners Propose VFlash: Accelerating Visual Planning Decoding for Physical AI
NVIDIA, in collaboration with several universities, has proposed VFlash, a visual planning decoding framework for Physical AI. By leveraging geometric priors to generate full trajectories and visual tokens in parallel, it eliminates the latency bottleneck of autoregressive token-by-token generation. On Jetson Thor, the framework speeds up the decoding stage by 12x to 42x, boosts overall robotic pipeline speed by 5.1x, and reduces average 3D displacement error by 42.6% in autonomous driving and robotic arm tasks.
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