This week NVIDIA published a set of open-source C++ sample code called "Do Inference Now Deploy" on its developer blog. The goal is singular: run AI models locally on RTX GPUs, without routing every request through the cloud.
What this is
The toolkit has three parts: ONNX Runtime (a universal cross-platform model format that lets the same file run on different systems), the TensorRT RTX execution layer (NVIDIA's own GPU inference accelerator, optimized for the RTX series), and a set of C++ samples. It addresses three long-standing developer complaints: non-portable model files, high migration costs between inference frameworks, and GPU acceleration locked to specific hardware. For practitioners, the significance is that local inference (using a trained model to make predictions or judgments) now has a vendor-endorsed reference implementation.
Industry view
Bulls argue this is the critical step turning on-device AI (running locally on the device rather than in the cloud) from concept into engineering reality — finance, healthcare, and manufacturing, where data compliance is strict, finally have a path not tethered to the cloud. The bear case is equally direct: the C++ bar is too high, most AI teams come from a Python background, and this looks more like a "show home for systems-level vendors" — far removed from day-to-day business. Add GPU memory constraints that choke truly large models, and most enterprises that actually deploy will still end up buying cloud compute.
Impact on regular people
For enterprise IT: improved feasibility of local inference means "self-built AI" stops being a non-starter for mid-sized companies over the next year or two — but only if hardware budgets and engineering headcount arrive together.
For individual careers: non-coding colleagues won't notice a difference in the short term. But if you're in product planning at a traditional software company, on-device AI capability is becoming one more line item in the feature menu — worth asking your engineering team to evaluate.
For the consumer market: Windows PCs and gaming laptops may become another vehicle for AI compute. AI is no longer just "that chat box in the browser."