**CosmoEdge** targets a gap that most inference stacks leave open: getting a video model off a workstation and onto the specific NPU box a customer already bought. It is a **C++17 edge AI engine** for video analytics and on-device VLM, published under **Apache-2.0** by the **cosmo-wander-ai** organization. Since going public on **9 June 2026** it has reached **577 stars and 150 forks**, and the current release is **v1.1.0**, published on **24 August 2026** — the third release in the project's history. The organizing idea is one engine architecture with per-target inference backends. A single build selects one backend and runs models compiled for that silicon, while the ingest, orchestration, event, and observability workflow stays identical across platforms. The v1.1 release covers **Sophon BM1688 and CV186X** (BMRT runtime, `.nn` artifacts), **Rockchip RK3576 and RV1126B** (RKNN runtime, target-specific `.rknn`, with RKLLM v1.3.0 packaged only for RK3576), and **x86 Linux and Windows** via ONNX Runtime. Apple Silicon macOS is explicitly a **Preview** path running under `linux/amd64` emulation for one local-video developer workflow — the README is careful to say this is not native performance evidence. **BM1684X is listed as planned**, not shipped. The VLM support is the part that makes this multimodal rather than a plain detector runtime. Prompt-driven vision runs alongside conventional CV pipelines: **VLM state judgment** and **GroundingDINO open-vocabulary detection**, with an optional VLM review step gating whether a detection alarm is actually reported — a practical way to cut false positives without a second full pipeline. The published benchmark pack validates **VLM at 0.1 FPS per channel across 6 channels on BM1688 and CV186X**, and **4 channels on RK3576**. RV1126B VLM is stated to be outside the v1.1 release claim. Those are deliberately narrow, protocol-bound numbers rather than headline throughput figures. Below the model layer, the Rockchip data path is where the engineering shows: targeted **DMA-BUF-to-RGA input**, persistent bound-input, native quantized output, and direct **YOLOv8 tensor decoding** with explicit fallbacks. One digest-pinned Rockchip builder shares the compiler and RKNN SDK while selecting an isolated MPP/RGA profile per chip, and every build exports target markers and `SHA256SUMS` under `build_output/`. Around that sits an application layer most serving projects skip entirely — browser-based **visual pipeline orchestration**, model import and validation, zones, counters, OSD, alarm snapshots, event history, and structured delivery over **REST, WebSocket, MQTT, or HTTP webhooks**. The caveats are real and mostly about boundaries. The engine and console are Apache-2.0, but **certified hardware, commercial preset models, and Model Guard 2.3 distribution protection sit outside that line** — the project states Open and Protected packages expose the same application features and differ only in model encryption and device-provisioning tooling, but teams should read that boundary before committing. The repository is also young, with **8 contributors** and 7 open issues, and the benchmark evidence is scoped to a recorded protocol and a controlled 72-hour dual-CV profile rather than broad field data. For teams deploying video AI onto Sophon or Rockchip hardware, it is an unusually complete starting point; for anyone on other silicon, the platform list is the first thing to check.