# Physical AI

ElevenLabs and NVIDIA are expanding their partnership to bring NVIDIA's Nemotron open models into ElevenAgents, ElevenLabs' conversational agent platform, and to add 15 MW of Vera Rubin capacity on top of existing Blackwell Ultra systems. On the ElevenAgents benchmark for realistic customer-support tasks, Nemotron 3.5 Super VL reached leading end-to-end task-completion accuracy at 70% lower cost than other leading open models. The two companies are also applying voice agents to Poland's national e-Health central appointment system, with an on-premises deployment that keeps citizen data and audio within government infrastructure.

ElevenLabs and NVIDIA are expanding their partnership to bring NVIDIA's Nemotron open models into ElevenAgents, ElevenLabs' conversational agent platform, and to add 15 MW of Vera Rubin capacity on top of existing Blackwell Ultra systems. On the ElevenAgents benchmark for realistic customer-support tasks, Nemotron 3.5 Super VL reached leading end-to-end task-completion accuracy at 70% lower cost than other leading open models. The two companies are also applying voice agents to Poland's national e-Health central appointment system, with an on-premises deployment that keeps citizen data and audio within government infrastructure.

NVIDIA and several Korean industry partners are pushing physical AI into production, spanning semiconductor engineering, autonomous driving and robot learning. In chip-scale thermal and mechanical analysis, Samsung Electronics combined AI-predicted effective material properties, coarser grids and GPU acceleration to cut one homogenization case from about 18 hours on a 32-core CPU fine-grid solve to 41 seconds on a GPU coarse-grid solve, roughly 1,600x faster; selected quantum chemical calculations on its sMOL platform ran up to 5x faster end to end.

NVIDIA and several Korean industry partners are pushing physical AI into production, spanning semiconductor engineering, autonomous driving and robot learning. In chip-scale thermal and mechanical analysis, Samsung Electronics combined AI-predicted effective material properties, coarser grids and GPU acceleration to cut one homogenization case from about 18 hours on a 32-core CPU fine-grid solve to 41 seconds on a GPU coarse-grid solve, roughly 1,600x faster; selected quantum chemical calculations on its sMOL platform ran up to 5x faster end to end.