Cloud AI targets one runtime. Edge AI targets fifty. Nuatara makes your model hardware-agnostic — so you deploy once and run everywhere, without rewriting for every chip.
| Topic | ☁ Cloud AI | ⚡ Edge AI |
|---|---|---|
| Runtime targetWhere the model runs | CUDA. Everything else is someone else's problem. | RK3588 · A311D · i.MX8 · Jetson · x86 — each different. |
| Deployment formatWhat you ship | TensorFlow SavedModel or PyTorch — your choice. | TensorRT · RKNN · TFLite · ONNX RT · OpenVINO. |
| ScalingHow you grow capacity | Add GPUs. The model never changes. | NPU vs CPU vs GPU — the right backend per board. |
| Drivers & BSPsLow-level system software | Managed infra. You never touch a kernel. | Every vendor ships a different Linux stack. |
| Precision & quantisationNumeric format of the model | No quantisation required. Just train and serve. | Calibration, accuracy validation, per-layer tuning. |
| Power envelopeEnergy budget per device | Unlimited. Plug in more servers. | 2 W on a camera module. Every mW counts. |
| ConnectivityNetwork dependency | Always-on internet required. | Offline-first. Inference must run locally. |
| LatencyTime to inference result | Round-trip to data centre. 50–200 ms. | Sub-10 ms on-device. Real-time control loops. |
Every chip you target adds a new layer of complexity. The stack multiplies.
Each silicon vendor ships its own inference runtime. A model that runs on one chip requires a completely different pipeline on the next.
Reducing model precision is not a single step — it requires calibration per model, per chip, and validation that accuracy survived the conversion.
Vendor BSPs, out-of-tree kernel modules, and NPU drivers that differ across board revisions. Updating one layer can break another.
Work done to deploy on one target rarely transfers to the next. Every new silicon requires starting the integration effort again.
Talk to us about your target silicon, your model, and your deployment constraints.
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