Choosing an SBC processor should be a benchmark problem. It isn’t.
The fastest chip can be the wrong choice if its software stack turns one working prototype into three weeks of driver hunting. Meanwhile, a slower processor can finish the same project first simply because the camera, operating system, and expansion board already work.
So this is not a ranking from the biggest benchmark number to the smallest. It is a list of five processors that make sense for five different kinds of SBC projects. The useful question is not Which chip wins?
It is: what kind of problem are you willing to inherit?
| Processor | Best fit | Example SBCs | The catch |
| Rockchip RK3588 | Balanced high-end Arm computing | KiwiPi 5 Pro, Orange Pi 5 Plus, Radxa ROCK 5B+ | Software support varies by board |
| Broadcom BCM2712 | General projects and first prototypes | Raspberry Pi 5, Compute Module 5 | No integrated AI accelerator |
| NVIDIA Jetson Orin Nano | CUDA-based edge AI and robotics | Jetson Orin Nano Super Developer Kit | Higher platform cost and power use |
| Intel N150 | Compact servers and x86 applications | ZimaBoard 2, LattePanda IOTA | Only four CPU cores |
| Texas Instruments AM67A | Industrial vision and real-time control | BeagleY-AI | More specialized development stack |
1. Rockchip RK3588: the best all-round high-performance Arm SoC
The RK3588 is what happens when an SBC needs to be several things at once: a small desktop, a media box, an edge server, and an AI development board.
Its CPU combines four Cortex-A76 performance cores with four Cortex-A55 efficiency cores. It also includes a Mali-G610 MP4 GPU, a 6 TOPS NPU, and unusually strong video hardware with support for 8K media. More importantly, the full RK3588 exposes enough high-speed I/O for NVMe storage, fast networking, multiple displays, and camera-heavy projects.
That makes it the most balanced processor in this list. It is not the fastest AI platform and it does not have the easiest software ecosystem. But it can handle a wider range of serious SBC workloads without immediately pushing the project toward a mini PC or a dedicated accelerator.
KiwiPi 5 Pro is one practical RK3588 example, particularly when dual 2.5GbE networking and NVMe expansion matter. Orange Pi 5 Plus and Radxa ROCK 5B+ use the same processor and offer different combinations of connectors, memory, and board support. The silicon is shared; the experience is not. Before choosing among them, compare the actual Linux image, cooling design, I/O routing, and vendor documentation – not just the RK3588 logo.
For a closer look at the architecture, NPU, video engine, and realistic use cases, see this guide to RK3588 specifications and performance.
2. Broadcom BCM2712: the best processor when the ecosystem matters more
BCM2712 is not the strongest SoC here. Raspberry Pi 5 may still be the easiest computer to build around.
The processor has four Cortex-A76 cores at 2.4 GHz and a VideoCore VII GPU. It is a major step up from Raspberry Pi 4, especially for interactive Linux use, compilation, browser workloads, and dual-display applications. Yet its real advantage is outside the chip: Raspberry Pi OS, mainline-minded drivers, HAT availability, tutorials, enclosures, cameras, and a community large enough to have already encountered your strange boot error.
It has no integrated NPU, and demanding storage or networking builds may expose the limits of a single PCIe lane. Those are real compromises. Still, a prototype that works today can be more valuable than a theoretically faster system that needs a custom kernel.
Raspberry Pi 5 is the obvious board example. Compute Module 5 brings the same processor to custom carrier boards and commercial designs.
3. NVIDIA Jetson Orin Nano: the best choice when CUDA is non-negotiable
Comparing Jetson Orin Nano with ordinary SBC processors by CPU score misses the point. You buy it for the GPU and, just as importantly, NVIDIA’s software stack.
The Jetson Orin Nano Super Developer Kit delivers up to 67 TOPS of AI performance, with 1,024 CUDA cores and 32 Tensor Cores. That makes it a much more natural platform for object detection, transformer inference, multi-camera robotics, and applications already built around CUDA, TensorRT, or DeepStream.
It is also an expensive way to blink LEDs. Power can scale up to 25 W, storage and peripherals add to the total cost, and it doesn’t exist for general desktop use. But if the project depends on an NVIDIA model pipeline, choosing a cheaper NPU often means paying the difference in engineering time.
The Jetson Orin Nano Super Developer Kit is the standard starting point. Production systems can later move to an Orin Nano module and a purpose-built carrier board.
Choose Jetson Orin Nano when: the project is primarily an AI or robotics system, not a general SBC with AI added later.
4. Intel N150: the best low-power x86 option
Intel N150 looks boring; that is exactly why it works.
It offers four x86-64 cores, four threads, a turbo frequency up to 3.6 GHz, and a 6 W processor base power. Native x86 support removes a surprising number of small obstacles: proprietary Linux packages, Windows applications, familiar virtualization tools, browser automation, and Docker images that were never tested on Arm.
ZimaBoard 2 turns the N150 into a compact server platform with dual 2.5GbE, SATA, and a full-size PCIe slot. LattePanda IOTA takes a more embedded route, adding onboard storage, GPIO, and an RP2040 coprocessor. Same processor, quite different jobs.
Four cores still impose a ceiling. N150 is excellent for Home Assistant, a small NAS, network services, light containers, and one or two carefully chosen server roles. It is not a replacement for a many-core workstation.
5. Texas Instruments AM67A: the best specialist for machine vision
AM67A is not a desktop processor pretending to be industrial hardware.
It combines four Cortex-A53 application cores with C7x DSPs, matrix-multiply acceleration rated at 4 TOPS, vision hardware, and Cortex-R5 cores for low-latency control. On paper, the CPU looks modest beside RK3588 or BCM2712. In a camera pipeline with deterministic I/O, that comparison becomes much less useful.
BeagleY-AI is the accessible SBC example. It exposes the AM67A in a familiar board format while retaining PCIe, camera connectivity, and BeagleBoard’s open-hardware approach. The trade-off is specialization: TI’s edge AI tools and heterogeneous processor architecture require more learning than installing another package on Raspberry Pi OS.
But for smart cameras, inspection systems, robotics control, and industrial vision, those specialized blocks are the reason to choose it.
Which SBC processor should you actually buy?
Start with the software and interfaces, then look at performance.
For the broadest high-performance Arm platform, RK3588 is the strongest all-round choice. BCM2712 wins when you want the shortest path from idea to working prototype. Jetson Orin Nano is the answer for CUDA-first AI. Intel N150 makes sense for small servers and x86-only software. AM67A belongs in projects where vision acceleration and real-time control justify a more specialized stack.
There is no universal winner here – and that is useful. A good SBC processor is not the chip with the largest number printed beside it. It is the one that leaves you solving your own problem instead of the platform’s.
