# OpenNVR 0.1.10: running an open-source NVR's AI on the Qualcomm Hexagon NPU > Source: > Published: 2026-10-31 10:15:25+00:00 OpenNVR is an open-source (AGPL) NVR that records and streams from any ONVIF or RTSP camera and runs the AI model you choose behind them: YOLO detection, pose, face recognition, plate OCR, open-vocabulary detection, and a vision-language agent that can look at a scene and reason about it. It runs on hardware you own, air-gapped if you need, with every inference audited. Version 0.1.10 is the first release with hardware acceleration flavours, and this post is about the one I have numbers for: Qualcomm's Hexagon NPU. Arduino Ventuno Q: Qualcomm Dragonwing IQ-8275 (QCS8275), eight ARM cores, a Hexagon NPU, Ubuntu 24.04. Small, fanless, the kind of thing that sits behind a few cameras in a shop or a plant. All measured on that board with the published container images. Full windows, settle times and caveats are in the [benchmark doc](https://github.com/open-nvr/open-nvr/blob/main/bench/ventuno-q-benchmark-2026-10-30.md). Tier-0 detector (YOLO, one frame): | Device | ms per frame | |---|---| | CPU, 8 threads | 164 | | Hexagon NPU, fp16 | 8.6 | In the full stack (streams, decode, tracking, the API, all on the same box): | Device | Cameras at 2 fps, no drops | Host idle | |---|---|---| | CPU | 4 (10 cameras drop half their frames) | ~4 % | | NPU | 16, 0 dropped | 46 % | The adapters that sit behind the detector, end-to-end on one 1080p JPEG: | Adapter | CPU | NPU | |---|---|---| | YOLO | 141 ms | 77 ms | | Pose | 108 ms | 64 ms | | Package detection | 100 ms | 60 ms | | Face recognition (InsightFace) | 443 ms | 62 ms | | Plate OCR | 248 ms | 62 ms | The NPU's own share of an adapter call is about 9 ms. The rest is JPEG decode and preprocessing on a busy CPU, so the adapter path is now bounded by image handling, not inference. Everything goes through ONNX Runtime's QNN execution provider, shipped as a plugin wheel (`onnxruntime-qnn`). There is no Qualcomm SDK to install on the host. The `-qualcomm` container gets the DSP device node, the DMA heap, and two host libraries bind-mounted, and that is it. ``` git clone https://github.com/open-nvr/open-nvr.git && cd open-nvr ./start.sh # OPENNVR_ACCEL=auto detects /dev/fastrpc-cdsp and picks the qualcomm flavour ``` `OPENNVR_ACCEL=cpu|intel|qualcomm` overrides the probe. A box with only a CPU gets exactly the images it got before. Compiled graphs are cached, so the second start skips the ~6 s compile. The pipeline reports which provider ONNX Runtime really ran on, and a request for an accelerator that silently fell back to CPU shows up as `requested != effective` in the metrics instead of being hidden. `providers=` list is silently ignored. It has to be attached by device with `add_provider_for_devices`.` python:slim` lacks `libatomic1`. The HTP backend fails to load and the session falls back to CPU with no error.`up`. ``` python -m detect_pipeline.bench --latency 200 --providers CPUExecutionProvider python -m detect_pipeline.bench --latency 200 --providers QNNExecutionProvider:htp ``` It reports median, p90 and min per device, names the provider that really ran, and exits non-zero if the accelerator fell back. NVIDIA, desktop GPUs and Jetson. If you have a Jetson, Rockchip or Hailo box you'd lend to a benchmark, I have one board and would like more.