Two years ago, running a meaningful ML model at the network edge meant either expensive dedicated hardware or embarrassingly slow inference that made the whole thing pointless. That’s changed. The Raspberry Pi AI HAT+ brings 13 TOPS of dedicated AI inference to the Pi 5 for under £100 total. NVIDIA’s Jetson Orin Nano delivers 40 TOPS starting around £200. Neither of these were realistic options for most edge deployments in 2023 — in 2026, they’re viable production choices for a wide range of industrial and IoT applications.

The question isn’t whether edge AI is possible. It’s which platform fits your specific application. Here’s how to think through it.

What you’re actually comparing

The Raspberry Pi 5 is a general-purpose single-board computer with the AI HAT+ as an optional add-on. The HAT adds Hailo-8 AI acceleration with 13 TOPS of dedicated INT8 inference performance. Without the HAT, the Pi 5’s quad-core ARM Cortex-A76 processor can still run smaller models through CPU inference — it’s just slower. With the HAT, you get hardware-accelerated inference while the CPU stays free for application logic.

The NVIDIA Jetson Orin Nano is a purpose-built AI compute module with a 1024-core Ampere GPU and up to 40 TOPS of AI performance built in. It’s not a general-purpose computer that you’ve added AI to — it’s an AI inference platform that also runs a full Linux environment. The software ecosystem (CUDA, TensorRT, DeepStream) is specifically built around production ML deployment.

Both run Linux. Both have active developer communities. The comparison isn’t between good and bad — it’s between different design philosophies and price-to-performance trade-offs.

When the Pi 5 makes sense

For prototyping and proof-of-concept work, the Pi 5 is almost always the right choice. You’re not committing to an expensive platform, the ecosystem is massive, and there’s more example code for common ML tasks on Raspberry Pi hardware than almost anything else. If you’re building a demo, validating a concept, or doing educational work, starting with Pi saves money and friction.

For production applications running models under 3 billion parameters with tolerances for 10-20 second inference latency, the Pi 5 plus AI HAT+ is genuinely deployable. Object detection models like YOLOv8 run at around 20 frames per second on the HAT — acceptable for most non-real-time inspection or monitoring use cases. Language models in the 1B-2B parameter range (SmolLM2, Gemma-2B) are viable for edge NLP tasks like classification, intent detection, or simple extraction.

The Pi 5 also wins on integration with the existing Raspberry Pi ecosystem. If your project uses GPIO for sensor interfacing, Camera Module v3 for vision, or any of the large catalogue of Pi HATs for industrial connectivity, staying in the Pi ecosystem is just easier. The community support for troubleshooting weird hardware interactions is incomparably better than anything in the Jetson ecosystem.

To be honest, the cost argument matters more than people admit. At £60 for the Pi 5 and £35-40 for the AI HAT+, you can deploy 10 edge nodes for roughly the cost of 3 Jetson Orin Nanos. For applications where you need many distributed low-power inference nodes rather than a few high-performance ones, the economics are completely different.

When the Jetson Orin Nano makes sense

Real-time video inference is the clearest Jetson use case. At 40 TOPS with a CUDA-enabled GPU, the Jetson Orin Nano can run multi-camera vision pipelines, multi-model inference chains, and streaming video analysis at frame rates that the Pi 5 with its 13 TOPS can’t match. If your application needs to process 1080p video streams in real time, or run several detection models simultaneously, you need the Jetson.

Production industrial deployments where reliability and software support matter long-term also favour the Jetson. NVIDIA offers JetPack SDK with security updates, CUDA and TensorRT libraries, and a formal support lifecycle that industrial customers can plan around. Raspberry Pi’s software support is good but less formally structured for the kind of multi-year operational commitments that go into factory floor or critical infrastructure deployments.

The Jetson also handles much larger models. Running a 7B parameter language model locally — for edge inference on sensitive data that can’t leave the building — requires the Jetson’s GPU. The Pi 5 simply doesn’t have the memory bandwidth or compute headroom.

If you’re building something that will go into 20 units in a factory, needs to run for five years with minimal maintenance, and requires real-time inference on high-resolution camera feeds, the Jetson Orin Nano is the right platform even though it costs more per unit.

The model format question

Whichever platform you choose, model format matters more than most guides acknowledge. For the Hailo on the Pi 5, you need your model compiled to the Hailo dataflow format using the Hailo Model Zoo or their compiler tools. For the Jetson, TensorRT engine files give the best performance, converted from ONNX. Both conversion processes work reliably for common architectures (ResNet, YOLO, EfficientDet, BERT-class) but can require experimentation for custom models or non-standard architectures.

Budget time for this step. Picking the hardware and ordering it is the easy part. Getting your specific model running efficiently on the target accelerator is where most edge AI projects spend unexpected hours.

A decision shortcut

If you’re not sure which to pick: start with a Pi 5 plus AI HAT+. It’s lower cost, faster to get running, and you’ll learn what your application actually needs before committing to production hardware. If the Pi 5 hits its limits — your model is too large, your latency requirements are too strict, or you need more simultaneous inference capacity — the Jetson Orin Nano is the natural step up. The migration isn’t painless, but it’s manageable, and you’ll make better platform choices with real performance data than you will from a spec sheet comparison.