NVIDIA Jetson: The Performance Standard
If you're running computer vision models at the edge, Jetson's probably on your list. It's the most popular choice for inference in robotics, autonomous systems, and video analytics. NVIDIA's CUDA architecture means your GPU acceleration actually works out of the box — not something you can take for granted in this space.
The Jetson family has options across price and power. The Nano runs at 5 watts and costs around $100. Orin NX pulls 25 watts and gives you 100 TFLOPS of AI performance. Orin AGX is where you go when you need the real power — 275 TFLOPS, but it's also $800+ and draws 60 watts. Most Ottawa startups we work with land on Orin NX. It's the Goldilocks option: enough performance for complex models, reasonable power draw, and the ecosystem is mature.
Jetson Reality Check
- Development is straightforward with JetPack SDK
- Community support is strong — you'll find answers to most problems
- GPU acceleration requires CUDA knowledge (steeper learning curve)
- Upfront cost is higher than ARM alternatives
ARM Processors: The Efficient Alternative
ARM boards like Raspberry Pi 5 and Qualcomm Snapdragon dominate the low-power space. You're looking at 1-5 watts, fanless operation, and costs between $35 and $200. The tradeoff? Raw performance is lower, but here's the thing — if your model's optimized properly, you won't notice.
ARM processors shine when you're doing inference with quantized models. TensorFlow Lite and ONNX Runtime both run well on ARM. We've deployed object detection models to Raspberry Pi 5 that process video at 15 FPS with 2% CPU usage. That's not a typo. The key is model optimization — you can't throw a 300MB model at ARM and expect miracles, but a 20MB quantized version? That'll work beautifully.
The real advantage shows up in volume deployments. If you're shipping 1000 units, the $150 difference per unit between Jetson and ARM adds up fast. Plus, fanless ARM boards are more reliable in harsh environments — construction sites, industrial floors, anywhere with dust or vibration.
Why Choose ARM
- Minimal power consumption (1-5W)
- Fanless operation = silent, reliable
- Lower upfront cost per unit
- Excellent for edge deployment at scale
Intel Edge Solutions: The Enterprise Option
Intel's Movidius line and their newer AI accelerators target industrial deployments. If you're integrating into existing enterprise systems or need deep support contracts, this is the path. Performance is solid, ecosystem integration with OpenVINO is smooth, and you get commercial support — which matters for mission-critical systems.
The catch? Cost and complexity. Intel solutions typically start at $300 and go up from there. You're also looking at more complex deployment pipelines. That said, if you're building for a manufacturing plant or hospital system, the overhead pays for itself in reliability and support.
Important: This Is Educational Guidance
This article provides informational guidance for understanding hardware options in edge AI deployment. Actual hardware selection depends on your specific requirements, budget constraints, and technical environment. Performance numbers are based on typical configurations and real-world testing, but your results may vary. Always prototype with your actual models before making large-scale purchasing decisions.
Making the Decision
Here's the framework we use with startups:
Understand Your Model Requirements
What's the actual model size after quantization? What inference speed do you need? Real-time processing at 30 FPS, or is 2 FPS acceptable?
Calculate Power and Thermal Budgets
How much power can your deployment environment handle? Fanless passive cooling, or is active cooling acceptable? This filters options fast.
Test with Your Real Models
Prototype on candidate hardware. Run your actual models. Measure latency, power draw, and CPU usage. Don't rely on spec sheets.
Factor in Total Cost of Ownership
Hardware cost is just the start. Add development time, deployment infrastructure, ongoing support, and update cycles. Cheap hardware with expensive development kills budgets.
What Ottawa Startups Are Actually Using
In our conversations with local tech companies, the pattern's clear: most start with Jetson Orin NX for development and prototyping. The development speed and CUDA support mean faster time-to-demo. Then, once the model's optimized and they understand real requirements, they either stick with Orin NX for production (if performance matters) or migrate to ARM-based platforms for cost efficiency and scale. A few go full Intel for enterprise integrations. There's no one-size-fits-all answer, but there's definitely a pattern.
Performance Considerations
Here's where real numbers matter. We tested a quantized ResNet50 model (15MB) across three platforms:
Jetson Orin NX
45ms latency
25W power draw, 30 FPS sustained
Raspberry Pi 5
280ms latency
5W power draw, 3 FPS sustained
Intel Movidius NCS2
65ms latency
2.5W power draw, 15 FPS sustained
Jetson wins on raw speed. But look at Intel — 65ms with minimal power. That's compelling for edge deployments where speed matters but you're running on battery or solar. And Raspberry Pi? It's 6x slower than Jetson, but uses 5x less power. For applications where latency isn't critical, that's the right choice.
