r/RockchipNPU

Reverse-Engineering the RK3588 NPU: Building an Open Compiler to Run GPT-2 at 36 tok/s

> Last year I posted about hacking the RK3588 NPU to run one vision encoder (previous post). This year I opened the whole thing up: reverse-engineered the register format, built an open compiler + runtime, and now GPT-2 and SigLIP run from PyTorch, ONNX, and JAX, no vendor SDK. > > Story: https://amohan.dev/blog/2026/opening-the-black-box-a-year-building-an-open-compiler-for-the-rk3588-npu/ > Report: https://amohan.dev/blog/2026/opennpu-v1-0-open-compiler-runtime-rk3588-npu/

reddit.com

u/one_does_not_just — 1 day ago

▲ 28 r/RockchipNPU+2 crossposts

Tried YOLO26n-depth in RK3576

Since Ultralytics officially supports exporting YOLO26n-Depth to RKNN, I tried running it on RK3576.

With a simple Python video inference test, I’m getting around 3–4 FPS for now. The code and model haven’t been optimized yet, so I think there is still quite a lot of room for improvement.

One thing I noticed is that the RKNN model exported directly by Ultralytics has only one output.

For comparison, the optimized YOLO11 / YOLOv10 models from Rockchip’s rknn_model_zoo use 9 outputs, which seems to be better optimized for the Rockchip NPU and moves some of the decoding/post-processing outside the model.

So 3–4 FPS is just the initial result. I’m going to see if YOLO26n-Depth can be optimized in a similar way.

Has anyone already tried optimizing YOLO26 or YOLO26-Depth for RK3576/RK3588?

u/n1k0n1k0n1 — 4 days ago