Physical AI is rapidly evolving, from next-generation software-defined autonomous vehicles (AVs) to humanoid robots. The challenge is no longer how to run a large language model (LLM), but how to enable high-fidelity reasoning, real-time multimodal interaction, and trajectory planning within strict power and latency envelopes. NVIDIA TensorRT Edge-LLM , a high-performance C++ inference runtime for LLMs and vision language models (VLMs) on embedded platforms, is designed to overcome these challenges.  As explained in this post, the latest TensorRT Edge-LLM release delivers a significant expansion in fundamental capabilities for NVIDIA DRIVE AGX Thor and NVIDIA Jetson Thor platforms. It introduces advanced edge architectures, including mixture of experts (MoE) , the NVIDIA Cosmos Reason 2 open planning model for physical AI, and Qwen3-TTS and Qwen-ASR models for embedded speech processing.…