Build Local AI Apps with C++ and NVIDIA TensorRT RTX Samples

NVIDIA Releases C++ Samples to Accelerate Local AI Apps

NVIDIA has released an open-source collection of C++ samples named Do Inference Now (DIN) Deploy, aimed at developers building native AI applications for Windows and Linux. The project provides a practical toolkit for integrating high-performance, local AI inference by combining the cross-platform ONNX Runtime with NVIDIA's TensorRT RTX execution provider. This initiative addresses a key challenge for developers: moving a trained model checkpoint into a hardware-accelerated desktop application efficiently and with portable code.

Technical Details and Performance Gains

The DIN Deploy workflow separates model preparation from application logic. Each sample uses a Python exporter to convert model checkpoints from Hugging Face into standard ONNX artifacts. The native C++ application then performs inference using the ONNX Runtime's session and tensor APIs, minimizing vendor-specific code in the main application path. This approach allows developers to integrate complex models without requiring a model-specific runtime. The repository provides CMake presets for easy setup on Windows, Linux, x86-64, and Arm64 platforms.

• Automatic Speech Recognition (ASR): The samples demonstrate significant acceleration, with NVIDIA Parakeet TDT achieving 206x real-time performance and Nemotron ASR Streaming hitting 39x on GPU.
• Interactive Segmentation: The Meta SAM 2.1 sample runs at 38.3 FPS on a DGX Spark GPU, a substantial improvement over the 0.5 FPS achieved on the CPU.
• Image Generation: A sample using FLUX.2-klein-4B shows graphics interop with Vulkan and DirectX and demonstrates how post-training quantization with NVIDIA Model Optimizer can create a drop-in replacement ONNX model for further performance boosts.

Impact on the Developer Ecosystem

By providing a robust C++-centric toolkit, NVIDIA is lowering the barrier for developers building performance-critical native applications, a segment often underserved by Python-focused AI frameworks. The standardization around ONNX promotes a more portable ecosystem while the TensorRT RTX provider ensures that applications can fully leverage the specialized hardware on RTX GPUs. This move helps solidify NVIDIA's platform as the de facto target for developers looking to add sophisticated AI features directly into consumer and professional desktop software.

Strategic Takeaway: NVIDIA's DIN Deploy is a strategic move to standardize the on-device AI development stack around its hardware, providing C++ developers with a direct, high-performance path from model training to native application deployment that bypasses more complex, multi-vendor frameworks.