RKNN3 SDK provides the complete software stack for deploying AI models on RK1820/RK1828/RK3572, including:
- RKNN3-Toolkit: PC-side software development kit for model conversion, inference, performance evaluation, etc.
- RKNN3 Runtime: On-board runtime library providing C/C++ programming interfaces for deploying RKNN models and accelerating AI applications.
- RKNN3 Model Zoo: Model conversion and deployment example repository, including reference implementations for CNN / LLM / VLM and other models.
Typical Workflow: Users first convert their trained models to RKNN format using RKNN3-Toolkit on a PC, then perform inference on the development board via the RKNN3 Runtime API.
- RK1820
- RK1828
- RK3572
Note:
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For RK3588/RK3576/RK3568/RK3566/RK3562 series, RV1103/RV1106, RV1103B/RV1106B, RV1126B, RK2118, please refer to:
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For RK1808/RV1109/RV1126/RK3399Pro, please refer to:
https://github.com/airockchip/rknn-toolkit
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RKNN3 Model Zoo provides more conversion and deployment examples
- Qwen2.5
- Qwen3
- Qwen3.5
- Youtu-LLM
- GLM-Edge
- MiniCPM5
- Nanbeige4.2
- FunctionGemma
- LFM2.5
- Qwen2.5-VL
- Qwen3-VL
- Qwen3.5-VL
- FastVLM
- InternVL3
- InternVL3.5
- MiMo-VL-RL
- Janus-Pro
- MiniCPM-V-4
- SmolVLM
- SmolVLM2
- UI_TARS
- gme-Qwen2-VL
- LocateAnything
- Qwen2.5-Omni (Thinker)
- Qwen3-Omni
- Qwen3.5-Omni
- Gemma4
- Qwen3-ASR
- WeNet (Conformer)
- Whisper
- SenseVoice
- Zipformer
- Qwen3_TTS
- VITS
- Qwen3-Embedding
- Qwen3-Reranker
- HY-MT1.5
- PaddleOCR VL
- SigLIP
- SigLIP2
- MetaCLIP2
- QA-CLIP
- DINOv2
- DINOv3
- Depth-Anything-V2-small
- Depth-Anything-V3
- Diffusion Policy
- GR00T
- MiniCPM-RobotManip
- MobileNetV1 / V2
- ResNet-50
- YOLOv5 / YOLOv6 / YOLOv8
- YOLO-World
- YOLO26 / YOLO26-Segment / YOLO26-Pose
For performance data, please refer to the Release Notes.
- RKNN3-Toolkit is not compatible with RKNN-Toolkit and RKNN-Toolkit2.
- Python 3.10
- Python 3.12
- Added RK182X multi-card cascade support
- Added Qwen3.5 model support
- Added Qwen3.5 and Gemma4 Prefix Caching support
- Added custom CPU operator support
- Added support for different context lengths across sessions
- Added Tie Word Embedding support
- Added RKNN3 Toolkit support for macOS (Beta)
- Optimized host-side memory usage during KVCache import and export
- Optimized
rknn3_mem_syncAPI performance - Optimized GPU memory usage during GRQ quantization
- Updated the usage of external GRQ quantization
- Changed the RKNN3 Toolkit model import method to use ONNX only (TensorFlow/TFLite/Caffe/DarkNet support removed)
For more information about previous releases, please refer to CHANGELOG.md.
- Redmine (Recommended for reporting issues. Please contact sales or an FAE to get a Redmine account)
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