NVIDIA 发布 Jetson Thor T2000 和 T3000 用于机器人及边缘 AI

The counterintuitive result is that skipping autoregressive decoding improves accuracy while speeding it up, since parallel output avoids compounding coordinate errors. Robotics and edge-AI developers working on Jetson-class hardware can use the open-source release to cut inference latency for manipulation and trajectory tasks. The parallel-decoding idea may also transfer to other fixed-structure prediction problems beyond robotics.

Jim Fan@DrJimFanSource

Pre-configured reference hardware could cut weeks of environment setup to days, lowering the barrier for labs that want to experiment with general-purpose robots rather than build their own stacks. Research teams at universities and corporate labs are the target buyers, with Stanford and ETH Zurich already expressing interest. Regional availability and pricing for Southeast Asian buyers have not been announced, so early access likely runs through Agile Robots' existing research channels.

NVIDIA 出了两款新 Jetson Thor 模组 T3000 和 T2000，算力比上代 AGX Orin 强，价格更亲民，适合边缘 AI 推理和多模态应用。