NVIDIA's platform runs most of the AI in production today, from training clusters to small modules mounted next to a sensor in the field. For the systems we build, it matters in two places: where models are served, and where data is produced.
In the data center and the cloud
- Serving models the agency controls. Open-weight models can run on the agency's own NVIDIA GPUs, on premises or in a government cloud region, so sensitive data never leaves the boundary. NVIDIA's inference software, such as NIM microservices and Triton Inference Server, packages a model behind a standard interface that is easy to deploy, scale and replace.
- Sized to the workload. We size inference to the real request volume and latency the mission needs, not to the largest configuration available.
At the edge
- AI next to the sensor. For field systems, NVIDIA Jetson modules run models on site: detecting, classifying and filtering readings before anything is sent over a constrained link. Only what matters crosses the network.
- Built to open interfaces. Edge processing is a module like any other: it sits behind a documented interface, so a sensor, a model or the compute module can each be upgraded without rebuilding the rest. Where a program calls for the SOSA Technical Standard, the edge design follows it.
Where it fits
- Computer vision on imagery and video from fixed and mobile sensors.
- Signal and sensor data classified at the edge, with summaries sent upstream.
- Private, on-premises language models for sensitive records.
Getting started
Tell us where the data is produced and where decisions are made, and we will propose where the models should run.
NVIDIA is a trademark of NVIDIA Corporation. Vonar AI is a member of the NVIDIA Inception program; it is not otherwise affiliated with or endorsed by NVIDIA.