<p>General-purpose robots and autonomous machines are moving from research labs to real-world mass-market deployment, creating demand for compact, power-efficient AI supercomputers capable of running foundation models at the edge.</p>
<p>To meet that need, NVIDIA has introduced the Blackwell-powered T3000 and T2000, new modules based on the <a href="https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/" target="_blank" rel="noopener">NVIDIA Thor</a> architecture that enable mass-market robotics and edge AI applications at scale.</p>
<p><a href="https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/" target="_blank" rel="noopener">Jetson AGX Thor</a> is powering this next generation of humanoid and robotic systems, with growing adoption across industries. Leading companies — including 1X, Agile Robots, Amazon Robotics, Boston Dynamics, FANUC, Hitachi and Techman Robot — are building on the platform.</p>
<p>The hardware underpinning those capabilities starts with the Jetson and IGX T3000 modules, which delivers 865 FP4 teraflops of AI compute in a compact form factor roughly half the size and power of the T5000. Jetson T3000 combines an NVIDIA Blackwell GPU, an eight-core Neoverse Arm CPU, 32GB of LPDDR5X memory and 273GB/s of memory bandwidth, along with 25 GbE connectivity. IGX T3000 delivers the same performance with integrated functional safety while seamlessly running the <a href="https://www.nvidia.com/en-us/ai-trust-center/halos/robotics/" target="_blank" rel="noopener">NVIDIA Halos for Robotics</a> full-stack safety system for robots operating alongside humans.</p>
<p>Despite its smaller footprint, the T3000 achieves similar inference performance of the T5000 for multimodal workloads, including large language models, vision language models, vision language action models and world foundation models. Migrating to T3000 helps reduce costs amid high memory prices.</p>

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