RKNN-Toolkit2

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

3313 stars · 386 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

RKNN-Toolkit2 is Rockchip's SDK for converting trained neural network models into RKNN format and deploying them on Rockchip NPU chips like RK3588. It includes PC-side model conversion and evaluation tools plus Python and C/C++ runtime APIs for on-device inference.

Use cases

• convert onnx or pytorch models to run on rockchip rk3588 npu
• deploy a yolov8 model on an embedded rockchip board
• run computer vision inference on rk3566 npu
• quantize a deep learning model for edge deployment
• benchmark model performance on rockchip npu
• run inference on rv1106 low-power vision chip

When to choose

• you are deploying AI models to Rockchip SoCs with an NPU (RK3588, RK3576, RV1106, etc.)
• you need model conversion, quantization, and on-device inference in one toolchain
• you want C/C++ or Python APIs for NPU-accelerated inference on embedded Linux

When to avoid

• you target non-Rockchip hardware such as NVIDIA GPUs, Jetson, or generic CPUs
• you are using older Rockchip chips like RK1808, RV1126, or RK3399Pro (use rknn-toolkit instead)
• you need to deploy large language models - use the separate RKNN-LLM SDK

Facets

library · maturity active

machine-learning llm-inference sdk compiler machine-learning embedded-systems computer-vision deep-learning python cpp embedded cli rockchip npu model-conversion rknn edge-ai inference linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="airockchip/rknn-toolkit2")