TL;DR: The Raspberry Pi 5 wins for general-purpose edge computing, thanks to its faster CPU, PCIe support, and vibrant ecosystem at a lower price. The Jetson Nano remains the better pick only when you need CUDA-accelerated GPU inference for deep learning workloads, though it is now aging hardware.
The State of Edge Computing in 2024
Edge computing has shifted from a niche hobbyist pursuit to a serious industrial strategy. Running inference and data processing close to the source reduces latency, cuts cloud bandwidth costs, and addresses privacy concerns. Two boards dominate conversations among developers: the Raspberry Pi 5, launched in late 2023, and NVIDIA’s Jetson Nano, originally released in 2019. Despite the age gap, the comparison keeps resurfacing because both target similar budgets and form factors.
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Specifications Head-to-Head
The Raspberry Pi 5 uses a Broadcom BCM2712 quad-core Arm Cortex-A76 CPU running at 2.4 GHz, paired with 4GB or 8GB of LPDDR4X RAM. It adds a PCIe 2.0 x1 lane, dual 4Kp60 HDMI output, USB 3.0, and Gigabit Ethernet. Critically, it lacks a dedicated GPU for compute — the VideoCore VII handles graphics, not neural network acceleration.
The Jetson Nano packs a quad-core Cortex-A57 CPU at 1.43 GHz and a 128-core Maxwell GPU with 4GB of LPDDR4 RAM. That GPU delivers roughly 472 GFLOPS in FP16, which is modest by modern standards but still enables real-time image classification and object detection through TensorRT and CUDA.
Performance and Software Ecosystem
In raw CPU tasks, the Pi 5 is roughly two to three times faster than the Nano. Compiling code, running web services, or handling sensor fusion favors the Pi. However, for deep learning inference, the Nano’s CUDA cores flip the result. A MobileNet SSD model that crawls on the Pi 5 can hit 20–30 FPS on the Nano with TensorRT optimization.
Software maturity matters just as much. Raspberry Pi OS benefits from a massive community, regular kernel updates, and compatibility with virtually every sensor and library. NVIDIA’s JetPack SDK is powerful but frozen on older Ubuntu and CUDA versions, and NVIDIA has effectively shifted developer attention to the Orin Nano, leaving the original Nano in maintenance mode.
Industry Impact and Buying Advice
The Pi 5 has become the default prototyping platform for smart cameras, industrial gateways, and retail analytics — especially when paired with a Coral USB Accelerator for lightweight AI. The Jetson Nano still appears in robotics courses and legacy vision pipelines where CUDA investment already exists. For new projects, most teams now choose the Pi 5 plus an accelerator, or jump straight to the Jetson Orin Nano if GPU compute is non-negotiable.
FAQ
Q: Can the Raspberry Pi 5 run AI models without extra hardware?
A: Yes, but only small models at low frame rates. For serious inference, add a Coral USB Accelerator or Hailo-8L module.
Q: Is the Jetson Nano still worth buying in 2024?
A: Only if you specifically need CUDA and TensorRT on a tight budget. Otherwise, the Orin Nano or Pi 5 with an accelerator is a better long-term investment.
Q: Which board uses less power?
A: The Jetson Nano draws about 5–10W under load, while the Pi 5 peaks near 12W with peripherals. Idle power is comparable, around 2–3W.
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