From Cloud to Edge: Deploying AI on Constrained Hardware

The "Why": Breaking the Cloud Dependence

Cloud AI is powerful, but it comes with a "latency tax" and privacy risks that are unacceptable for mission-critical robotics or healthcare applications. The solution lies in Edge AI: running models locally on devices like the NVIDIA Jetson or Raspberry Pi.

By moving inference to the edge, we achieve zero-latency decision making and guaranteed data privacy—essential for the next generation of autonomous agents.

The Hardware Landscape

Choosing the right silicon is step zero. We generally look at two main contenders in the constrained hardware space:

Feature	Raspberry Pi 5 (The Generalist)	NVIDIA Jetson Orin Nano (The Specialist)
Architecture	CPU-heavy (ARM Cortex)	GPU-heavy (Ampere Architecture)
Best For	Light inference, CPU models (TFLite)	Computer Vision, SLMs, Robotics
Acceleration	NPU (in newer chips)	CUDA & TensorRT (Industry Standard)

Engineer's Note: While the RPi is excellent for prototyping, if your pipeline involves heavy matrix multiplication (Transformers/CNNs), the CUDA cores on the Jetson are non-negotiable.

Core Techniques: Shrinking the Giants

You cannot simply "run" a 7B parameter model on a 4GB RAM device. You must compress the signal.

1. Quantization (FP32 $\rightarrow$ INT8)

Standard models use 32-bit floating-point numbers. Quantization maps these to 8-bit integers, reducing model size by 4x with negligible accuracy loss.

import torch

# Example of dynamic quantization in PyTorch
quantized_model = torch.quantization.quantize_dynamic(
    model, 
    {torch.nn.Linear}, 
    dtype=torch.qint8
)