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This release streamlines the deployment of large language models into native C++ environments by eliminating ONNX export steps, directly addressing latency and dependency concerns critical for robotics and edge computing workloads. By leveraging OpenAI Codex agents to build its own inference stack, NVIDIA demonstrates a strategic shift toward optimizing AI hardware utilization through highly efficient, non-Python runtime paths.

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NVIDIA Releases TensorRT Model Connect in Public Preview: Hugging Face Checkpoint to Native C++ Inference in Two Commands

MarkTechPostAugust 18, 20263 min read

NVIDIA has released TensorRT Model Connect (TRTMC) in public preview, an Apache-2.0 project that takes a supported Hugging Face or local checkpoint to end-to-end TensorRT inference in two commands, with no intermediate ONNX export. The build emits a versioned .bundle artifact that runs through native C++ task APIs, so inference executes without PyTorch in the runtime path. NVIDIA's July 29, 2026 GB300 snapshot covers 105 release profiles across 76 model families. The post NVIDIA Releases TensorRT Model Connect in Public Preview: Hugging Face Checkpoint to Native C++ Inference in Two Commands appeared first on MarkTechPost.

NVIDIA has released TensorRT Model Connect (TRTMC) in public preview, an open-source project that takes a supported Hugging Face or local checkpoint to end-to-end TensorRT inference in two commands. There is no intermediate ONNX export step. The build produces a versioned .bundle artifact that runs through native C++ task APIs, so inference can execute in a C++ service, embedded…

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