Build Local AI Apps with C++ and NVIDIA TensorRT RTX Samples
What it is
This is an NVIDIA technical blog post introducing the open-source 'Do Inference Now (DIN) Deploy' repository. It describes how to use C++ samples to combine ONNX Runtime with the TensorRT RTX execution provider for accelerating local AI inference tasks like speech recognition and image generation on Windows and Linux.
The problem it solves
The complexity and lack of standardized, portable tools for deploying accelerated AI models directly into native C++ desktop applications on consumer hardware.
What you could use it for
- Developing native C++ applications for real-time speech recognition (ASR) using models like Whisper or Parakeet TDT.
- Implementing interactive image segmentation with Meta SAM 2.1 in local desktop applications.
- Building local generative AI tools using FLUX models with Vulkan/DirectX graphics interop.
- Optimizing ONNX models for NVIDIA RTX GPUs using the TensorRT RTX execution provider.
Why it matters
Enables developers to build high-performance, local-first AI applications without cloud dependency, leveraging native C++ and GPU acceleration for significant speedups (up to 206x real-time for ASR).
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.