Details
- NVIDIA AI announced the public preview of TensorRT Model Connect, a new path from Hugging Face models to TensorRT inference.
- The workflow lets developers take supported Hugging Face models to end-to-end TensorRT deployment in just two commands, reducing setup complexity.
- The tool removes the need for an intermediate ONNX export step, simplifying the traditional PyTorch or Transformers to TensorRT pipeline.
- Resulting inference bundles can be executed through native C++ APIs, targeting production-grade, low-latency deployment on NVIDIA GPUs.
- NVIDIA notes that the entire TensorRT Model Connect project was built using OpenAI Codex, highlighting AI-assisted software development in its tooling stack.
- The release follows earlier experimental access shared with developers and now opens the feature more broadly for public testing and feedback.
- By tightly integrating with Hugging Face formats, TensorRT Model Connect complements existing TensorRT-LLM and NeMo deployment paths for large language models.
Impact
TensorRT Model Connect broadens NVIDIA’s appeal to AI developers by streamlining deployment from widely used Hugging Face models into high-performance TensorRT runtimes. By eliminating ONNX conversion and exposing native C++ APIs, it lowers operational friction for teams moving from experimentation to production and strengthens NVIDIA’s position against other inference stacks that rely on more complex export chains.
