framework
NVIDIA inference runtime used in the generated application's pipeline between preprocessing and segmentation postprocessing.
No evidence-backed relationships are recorded.
NVIDIA's CUDA buffer backend lets a standard ROS 2 message field carry GPU memory, and the same host, the same device, the same Linux user and a supported RMW all have to line up before the copies actually disappear.
Reality
NVIDIA's TensorRT backend now creates per-rank contexts and NCCL communicators inside one Triton instance, and the client sends a single gRPC request to a model name. The GPU count is fixed at compile time.
Reality
Python does the preparation and the shipped binary just loads a bundle, which is a clean boundary. The per-model implementations behind it are written by coding agents under human review, and that is the part to read.
Reality
AWS, NVIDIA and Heidi Health report holding sub-second transcription while cutting 16 GPU instances to four. Per-GPU throughput rose only about 1.5x, so the rest of that saving came out of provisioning headroom.
Reality
A vendor walkthrough has a coding agent build an endoscopic segmentation app from HoloHub examples, with the CLI as the shared execution surface and named skills as the spec.
Reality