As a developer, I’m always on the lookout for hardware that can handle my workflow efficiently—whether it’s compiling code, running multiple containers, or testing applications. The Rockchip RK3588 is one of those System-on-Chip (SoC) platforms that has caught my attention, and after some hands-on experience, I want to share why it might be a great choice for programmers.
Why the RK3588 Stands Out
The RK3588 is a high-performance ARM-based SoC featuring:
1) Quad-core Cortex-A76 + Quad-core Cortex-A55 (big.LITTLE architecture)
2) Up to 8GB/16GB LPDDR4/LPDDR4X RAM (depending on the board)
3)Mali-G610 MP4 GPU (useful for light GPU-accelerated tasks)
4) 6 TOPS NPU (for AI/ML workloads)
5)Multiple PCIe, USB 3.1, SATA, and HDMI 2.1 interfaces
For programming purposes, this means:
✔ Faster compilation times compared to older ARM boards (like the Raspberry Pi 4).
✔ Smooth multitasking when running IDEs, Docker, and databases simultaneously.
✔ Good support for Linux distributions, making it a solid dev environment.
My Development Setup with the RK3588
I’ve been using an RK3588-based single-board computer (SBC) as a secondary development machine, and here’s how I’ve configured it:
1. OS Choice: Debian or Ubuntu?
Most RK3588 boards (like the Radxa Rock 5B/Orange Pi 5 Plus or Kiwi Pi 5 Pro) support Armbian, Debian, and Ubuntu. I’m currently running Ubuntu 22.04 LTS because of its better package support.
2. Running Docker & Kubernetes
Since I work with containers, I installed Docker and kubectl without issues. The RK3588 handles multiple containers smoothly, though I wouldn’t recommend it for large-scale Kubernetes clusters—it’s great for local testing.
bash
sudo apt install docker.io
sudo usermod -aG docker $USER
3. Compiling Code: GCC & LLVM Performance
I tested compiling a medium-sized C++ project (~10k lines) with GCC 11, and it was noticeably faster than on a Raspberry Pi 4. The A76 cores make a difference.
bash`
time make -j$(nproc) # Using all cores
4. Python & AI Development
The 6 TOPS NPU is interesting for lightweight ML inference. I tested TensorFlow Lite and ONNX runtime, and while it won’t replace a high-end GPU, it’s usable for prototyping.
python
import tensorflow as tf
print(tf.lite.Interpreter(model_path="model.tflite").get_input_details())
5. VS Code & Remote Development
I mostly use VS Code with Remote-SSH to connect from my main machine. The RK3588 handles it well, though heavy extensions (like Rust Analyzer) can slow it down.
Limitations to Consider
ARM64-only: Some proprietary software (like some JetBrains IDEs) doesn’t have native ARM builds.
GPU drivers: While improving, Mali GPU support isn’t as polished as x86 systems.
Power consumption: Under full load, it draws more power than a Raspberry Pi.
*Final Thoughts: * Is the RK3588 Good for Programmers?
If you need a powerful ARM-based dev board for Linux development, Docker, or light AI workloads, the RK3588 is a strong contender. It won’t replace a high-end x86 workstation, but for embedded development, learning ARM architecture, or as a secondary machine, it’s excellent.
I’m focusing on using mine for Kubernetes testing and cross-compilation, and so far, it’s been a reliable tool. If you’ve tried the RK3588 for development, I’d love to hear your thoughts!